【2011-2016】 NIPS汇总

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Advances in Neural Information Processing Systems 24 (NIPS 2011)

The papers below appear in Advances in Neural Information Processing Systems 24 edited by J. Shawe-Taylor and R.S. Zemel and P.L. Bartlettand F. Pereira and K.Q. Weinberger.
They are proceedings from the conference, "Neural Information Processing Systems 2011."

Maximum Margin Multi-Instance Learning Hua Wang, Heng Huang, Farhad Kamangar, Feiping Nie, Chris H. Ding
Shaping Level Sets with Submodular Functions Francis R. Bach
Nonlinear Inverse Reinforcement Learning with Gaussian Processes Sergey Levine, Zoran Popovic, Vladlen Koltun
Video Annotation and Tracking with Active Learning Carl Vondrick, Deva Ramanan
On U-processes and clustering performance Stéphan J. Clémençcon
Penalty Decomposition Methods for Rank Minimization Yong Zhang, Zhaosong Lu
Sparse Manifold Clustering and Embedding Ehsan Elhamifar, René Vidal
Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction Siwei Lyu
Image Parsing with Stochastic Scene Grammar Yibiao Zhao, Song-chun Zhu
A Reinforcement Learning Theory for Homeostatic Regulation Mehdi Keramati, Boris S. Gutkin
Learning large-margin halfspaces with more malicious noise Phil Long, Rocco Servedio
On Strategy Stitching in Large Extensive Form Multiplayer Games Richard G. Gibson, Duane Szafron
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Philipp Krähenbühl, Vladlen Koltun
Transfer Learning by Borrowing Examples for Multiclass Object Detection Joseph J. Lim, Ruslan R. Salakhutdinov, Antonio Torralba
Environmental statistics and the trade-off between model-based and TD learning in humans Dylan A. Simon, Nathaniel D. Daw
Variational Learning for Recurrent Spiking Networks Danilo J. Rezende, Daan Wierstra, Wulfram Gerstner
Multiple Instance Learning on Structured Data Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard D. Lawrence
Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds Nitesh Shroff, Pavan Turaga, Rama Chellappa
A Global Structural EM Algorithm for a Model of Cancer Progression Ali Tofigh, Erik Sj̦lund, Mattias H̦glund, Jens Lagergren
Action-Gap Phenomenon in Reinforcement Learning Amir-massoud Farahmand
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition Nobuyuki Morioka, Shin'ichi Satoh
Matrix Completion for Multi-label Image Classification Ricardo S. Cabral, Fernando Torre, Joao P. Costeira, Alexandre Bernardino
Multi-View Learning of Word Embeddings via CCA Paramveer Dhillon, Dean P. Foster, Lyle H. Ungar
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent Shinichi Nakajima, Masashi Sugiyama, S. D. Babacan
Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron Ryota Kobayashi, Yasuhiro Tsubo, Petr Lansky, Shigeru Shinomoto
Additive Gaussian Processes David K. Duvenaud, Hannes Nickisch, Carl E. Rasmussen
Inferring Interaction Networks using the IBP applied to microRNA Target Prediction Hai-son P. Le, Ziv Bar-joseph
Semantic Labeling of 3D Point Clouds for Indoor Scenes Hema S. Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena
Learning Higher-Order Graph Structure with Features by Structure Penalty Shilin Ding, Grace Wahba, Xiaojin Zhu
Analysis and Improvement of Policy Gradient Estimation Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
Dimensionality Reduction Using the Sparse Linear Model Ioannis A. Gkioulekas, Todd Zickler
Robust Multi-Class Gaussian Process Classification Daniel Hernández-lobato, Jose M. Hernández-lobato, Pierre Dupont
Maximum Margin Multi-Label Structured Prediction Christoph H. Lampert
Extracting Speaker-Specific Information with a Regularized Siamese Deep Network Ke Chen, Ahmad Salman
Thinning Measurement Models and Questionnaire Design Ricardo Silva
Inductive reasoning about chimeric creatures Charles Kemp
Optimal Reinforcement Learning for Gaussian Systems Philipp Hennig
A Denoising View of Matrix Completion Weiran Wang, Miguel Á. Carreira-Perpiñán, Zhengdong Lu
Efficient Online Learning via Randomized Rounding Nicolò Cesa-bianchi, Ohad Shamir
Efficient Methods for Overlapping Group Lasso Lei Yuan, Jun Liu, Jieping Ye
Differentially Private M-Estimators Jing Lei
Multiple Instance Filtering Kamil A. Wnuk, Stefano Soatto
Phase transition in the family of p-resistances Morteza Alamgir, Ulrike V. Luxburg
Convergent Bounds on the Euclidean Distance Yoonho Hwang, Hee-kap Ahn
Heavy-tailed Distances for Gradient Based Image Descriptors Yangqing Jia, Trevor Darrell
RTRMC: A Riemannian trust-region method for low-rank matrix completion Nicolas Boumal, Pierre-antoine Absil
Expressive Power and Approximation Errors of Restricted Boltzmann Machines Guido F. Montufar, Johannes Rauh, Nihat Ay
History distribution matching method for predicting effectiveness of HIV combination therapies Jasmina Bogojeska
Semi-supervised Regression via Parallel Field Regularization Binbin Lin, Chiyuan Zhang, Xiaofei He
Object Detection with Grammar Models Ross B. Girshick, Pedro F. Felzenszwalb, David A. McAllester
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning Eric Moulines, Francis R. Bach
On fast approximate submodular minimization Stefanie Jegelka, Hui Lin, Jeff A. Bilmes
Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits Matthias S. Keil
Efficient anomaly detection using bipartite k-NN graphs Kumar Sricharan, Alfred O. Hero
Projection onto A Nonnegative Max-Heap Jun Liu, Liang Sun, Jieping Ye
Improving Topic Coherence with Regularized Topic Models David Newman, Edwin V. Bonilla, Wray Buntine
A Two-Stage Weighting Framework for Multi-Source Domain Adaptation Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye
An ideal observer model for identifying the reference frame of objects Joseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths
Generalized Beta Mixtures of Gaussians Artin Armagan, Merlise Clyde, David B. Dunson
Large-Scale Sparse Principal Component Analysis with Application to Text Data Youwei Zhang, Laurent E. Ghaoui
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC Trung T. Pham, Tat-jun Chin, Jin Yu, David Suter
\theta-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding Congcong Li, Ashutosh Saxena, Tsuhan Chen
Crowdclustering Ryan G. Gomes, Peter Welinder, Andreas Krause, Pietro Perona
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition Jia Deng, Sanjeev Satheesh, Alexander C. Berg, Fei Li
Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification Ichiro Takeuchi, Masashi Sugiyama
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers Luca Oneto, Davide Anguita, Alessandro Ghio, Sandro Ridella
Relative Density-Ratio Estimation for Robust Distribution Comparison Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
Solving Decision Problems with Limited Information Denis D. Maua, Cassio Campos
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation Zhouchen Lin, Risheng Liu, Zhixun Su
Learning a Tree of Metrics with Disjoint Visual Features Kristen Grauman, Fei Sha, Sung Ju Hwang
Efficient inference in matrix-variate Gaussian models with \iid observation noise Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt
On Causal Discovery with Cyclic Additive Noise Models Joris M. Mooij, Dominik Janzing, Tom Heskes, Prof. Bernhard Schölkopf
Learning to Agglomerate Superpixel Hierarchies Viren Jain, Srinivas C. Turaga, K Briggman, Moritz N. Helmstaedter, Winfried Denk, H. S. Seung
A Convergence Analysis of Log-Linear Training Simon Wiesler, Hermann Ney
Shallow vs. Deep Sum-Product Networks Olivier Delalleau, Yoshua Bengio
Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment Sebastian A. Kurtek, Anuj Srivastava, Wei Wu
From Bandits to Experts: On the Value of Side-Observations Shie Mannor, Ohad Shamir
Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Benjamin Recht, Christopher Re, Stephen Wright, Feng Niu
Clustered Multi-Task Learning Via Alternating Structure Optimization Jiayu Zhou, Jianhui Chen, Jieping Ye
Why The Brain Separates Face Recognition From Object Recognition Joel Z. Leibo, Jim Mutch, Tomaso Poggio
Reinforcement Learning using Kernel-Based Stochastic Factorization Andre S. Barreto, Doina Precup, Joelle Pineau
k-NN Regression Adapts to Local Intrinsic Dimension Samory Kpotufe
Learning unbelievable probabilities Xaq Pitkow, Yashar Ahmadian, Ken D. Miller
A Machine Learning Approach to Predict Chemical Reactions Matthew A. Kayala, Pierre F. Baldi
Dynamical segmentation of single trials from population neural data Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance Carsten Rother, Martin Kiefel, Lumin Zhang, Prof. Bernhard Schölkopf, Peter V. Gehler
Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations Emin Orhan, Robert A. Jacobs
Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness Rémi Munos
Reconstructing Patterns of Information Diffusion from Incomplete Observations Flavio Chierichetti, David Liben-nowell, Jon M. Kleinberg
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection Richard Socher, Eric H. Huang, Jeffrey Pennin, Christopher D. Manning, Andrew Y. Ng
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity Nir Ailon
Modelling Genetic Variations using Fragmentation-Coagulation Processes Yee W. Teh, Charles Blundell, Lloyd Elliott
Prediction strategies without loss Michael Kapralov, Rina Panigrahy
Data Skeletonization via Reeb Graphs Xiaoyin Ge, Issam I. Safa, Mikhail Belkin, Yusu Wang
Information Rates and Optimal Decoding in Large Neural Populations Kamiar R. Rad, Liam Paninski
Selective Prediction of Financial Trends with Hidden Markov Models Dmitry Pidan, Ran El-Yaniv
Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning Xinggang Wang, Xiang Bai, Xingwei Yang, Wenyu Liu, Longin J. Latecki
Distributed Delayed Stochastic Optimization Alekh Agarwal, John C. Duchi
Greedy Algorithms for Structurally Constrained High Dimensional Problems Ambuj Tewari, Pradeep K. Ravikumar, Inderjit S. Dhillon
Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction Elad Hazan, Satyen Kale
Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries Zhen J. Xiang, Hao Xu, Peter J. Ramadge
Minimax Localization of Structural Information in Large Noisy Matrices Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data Vijay Mahadevan, Chi W. Wong, Jose C. Pereira, Tom Liu, Nuno Vasconcelos, Lawrence K. Saul
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression Sham M. Kakade, Varun Kanade, Ohad Shamir, Adam Kalai
On the Analysis of Multi-Channel Neural Spike Data Bo Chen, David E. Carlson, Lawrence Carin
Learning Eigenvectors for Free Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth
Noise Thresholds for Spectral Clustering Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
The Kernel Beta Process Lu Ren, Yingjian Wang, Lawrence Carin, David B. Dunson
Statistical Performance of Convex Tensor Decomposition Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima
Probabilistic amplitude and frequency demodulation Richard Turner, Maneesh Sahani
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators Dominique C. Perrault-joncas, Marina Meila
Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons Yan Karklin, Eero P. Simoncelli
Complexity of Inference in Latent Dirichlet Allocation David Sontag, Dan Roy
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng
Lower Bounds for Passive and Active Learning Maxim Raginsky, Alexander Rakhlin
Stochastic convex optimization with bandit feedback Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
Structure Learning for Optimization Shulin Yang, Ali Rahimi
Inverting Grice's Maxims to Learn Rules from Natural Language Extractions Mohammad S. Sorower, Janardhan R. Doppa, Walker Orr, Prasad Tadepalli, Thomas G. Dietterich, Xiaoli Z. Fern
Active Classification based on Value of Classifier Tianshi Gao, Daphne Koller
Group Anomaly Detection using Flexible Genre Models Liang Xiong, Barnabás Póczos, Jeff G. Schneider
Approximating Semidefinite Programs in Sublinear Time Dan Garber, Elad Hazan
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements Andrew E. Waters, Aswin C. Sankaranarayanan, Richard Baraniuk
Budgeted Optimization with Concurrent Stochastic-Duration Experiments Javad Azimi, Alan Fern, Xiaoli Z. Fern
Online Submodular Set Cover, Ranking, and Repeated Active Learning Andrew Guillory, Jeff A. Bilmes
Structured sparse coding via lateral inhibition Arthur D. Szlam, Karol Gregor, Yann L. Cun
Sparse Filtering Jiquan Ngiam, Zhenghao Chen, Sonia A. Bhaskar, Pang W. Koh, Andrew Y. Ng
Divide-and-Conquer Matrix Factorization Lester W. Mackey, Michael I. Jordan, Ameet Talwalkar
Im2Text: Describing Images Using 1 Million Captioned Photographs Vicente Ordonez, Girish Kulkarni, Tamara L. Berg
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference David Wingate, Noah Goodman, Andreas Stuhlmueller, Jeffrey M. Siskind
Collective Graphical Models Daniel R. Sheldon, Thomas G. Dietterich
Metric Learning with Multiple Kernels Jun Wang, Huyen T. Do, Adam Woznica, Alexandros Kalousis
ShareBoost: Efficient multiclass learning with feature sharing Shai Shalev-shwartz, Yonatan Wexler, Amnon Shashua
Active dendrites: adaptation to spike-based communication Balazs B. Ujfalussy, Máté Lengyel
Message-Passing for Approximate MAP Inference with Latent Variables Jiarong Jiang, Piyush Rai, Hal Daume
A More Powerful Two-Sample Test in High Dimensions using Random Projection Miles Lopes, Laurent Jacob, Martin J. Wainwright
Orthogonal Matching Pursuit with Replacement Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon
Composite Multiclass Losses Elodie Vernet, Mark D. Reid, Robert C. Williamson
Beating SGD: Learning SVMs in Sublinear Time Elad Hazan, Tomer Koren, Nati Srebro
Greedy Model Averaging Dong Dai, Tong Zhang
Large-Scale Category Structure Aware Image Categorization Bin Zhao, Fei Li, Eric P. Xing
On the accuracy of l1-filtering of signals with block-sparse structure Fatma K. Karzan, Arkadi S. Nemirovski, Boris T. Polyak, Anatoli Juditsky
Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach Qibin Zhao, Cesar F. Caiafa, Danilo P. Mandic, Liqing Zhang, Tonio Ball, Andreas Schulze-bonhage, Andrzej S. Cichocki
Finite Time Analysis of Stratified Sampling for Monte Carlo Alexandra Carpentier, Rémi Munos
Monte Carlo Value Iteration with Macro-Actions Zhan Lim, Lee Sun, David Hsu
Structured Learning for Cell Tracking Xinghua Lou, Fred A. Hamprecht
Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories Cristina Savin, Peter Dayan, Máté Lengyel
Algorithms and hardness results for parallel large margin learning Phil Long, Rocco Servedio
Portmanteau Vocabularies for Multi-Cue Image Representation Fahad S. Khan, Joost Weijer, Andrew D. Bagdanov, Maria Vanrell
Boosting with Maximum Adaptive Sampling Charles Dubout, Francois Fleuret
Gaussian Process Training with Input Noise Andrew Mchutchon, Carl E. Rasmussen
Empirical models of spiking in neural populations Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities Angela Yao, Juergen Gall, Luc V. Gool, Raquel Urtasun
Bayesian Partitioning of Large-Scale Distance Data David Adametz, Volker Roth
From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models Skander Mensi, Richard Naud, Wulfram Gerstner
On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference Guy Broeck
Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices Xianxing Zhang, Lawrence Carin, David B. Dunson
An Exact Algorithm for F-Measure Maximization Krzysztof J. Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
Co-regularized Multi-view Spectral Clustering Abhishek Kumar, Piyush Rai, Hal Daume
Sequence learning with hidden units in spiking neural networks Johanni Brea, Walter Senn, Jean-pascal Pfister
Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis Shuai Huang, Jing Li, Jieping Ye, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman
A blind sparse deconvolution method for neural spike identification Chaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli
How Do Humans Teach: On Curriculum Learning and Teaching Dimension Faisal Khan, Bilge Mutlu, Xiaojin Zhu
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Mark Schmidt, Nicolas L. Roux, Francis R. Bach
Joint 3D Estimation of Objects and Scene Layout Andreas Geiger, Christian Wojek, Raquel Urtasun
Spatial distance dependent Chinese restaurant processes for image segmentation Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei
Pylon Model for Semantic Segmentation Victor Lempitsky, Andrea Vedaldi, Andrew Zisserman
t-divergence Based Approximate Inference Nan Ding, Yuan Qi, S.v.n. Vishwanathan
Learning person-object interactions for action recognition in still images Vincent Delaitre, Josef Sivic, Ivan Laptev
Submodular Multi-Label Learning James Petterson, Tibério S. Caetano
Uniqueness of Belief Propagation on Signed Graphs Yusuke Watanabe
Higher-Order Correlation Clustering for Image Segmentation Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo
Optimal learning rates for least squares SVMs using Gaussian kernels Mona Eberts, Ingo Steinwart
Learning Auto-regressive Models from Sequence and Non-sequence Data Tzu-kuo Huang, Jeff G. Schneider
Committing Bandits Loc X. Bui, Ramesh Johari, Shie Mannor
Energetically Optimal Action Potentials Martin B. Stemmler, Biswa Sengupta, Simon Laughlin, Jeremy Niven
Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning Taiji Suzuki
See the Tree Through the Lines: The Shazoo Algorithm Fabio Vitale, Nicolò Cesa-bianchi, Claudio Gentile, Giovanni Zappella
The Fast Convergence of Boosting Matus J. Telgarsky
Multi-armed bandits on implicit metric spaces Aleksandrs Slivkins
Learning Anchor Planes for Classification Ziming Zhang, Lubor Ladicky, Philip Torr, Amir Saffari
Infinite Latent SVM for Classification and Multi-task Learning Jun Zhu, Ning Chen, Eric P. Xing
Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines Matthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus
Universal low-rank matrix recovery from Pauli measurements Yi-kai Liu
Better Mini-Batch Algorithms via Accelerated Gradient Methods Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan
Adaptive Hedge Tim V. Erven, Wouter M. Koolen, Steven D. Rooij, Peter Grünwald
Agnostic Selective Classification Yair Wiener, Ran El-Yaniv
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs Armen Allahverdyan, Aram Galstyan
PAC-Bayesian Analysis of Contextual Bandits Yevgeny Seldin, Peter Auer, John S. Shawe-taylor, Ronald Ortner, François Laviolette
Bayesian Spike-Triggered Covariance Analysis Il Memming Park, Jonathan W. Pillow
Non-conjugate Variational Message Passing for Multinomial and Binary Regression David A. Knowles, Tom Minka
Learning to Search Efficiently in High Dimensions Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong, Thomas S. Huang
A Non-Parametric Approach to Dynamic Programming Oliver B. Kroemer, Jan R. Peters
Advice Refinement in Knowledge-Based SVMs Gautam Kunapuli, Richard Maclin, Jude W. Shavlik
Kernel Bayes' Rule Kenji Fukumizu, Le Song, Arthur Gretton
Transfer from Multiple MDPs Alessandro Lazaric, Marcello Restelli
Sparse Bayesian Multi-Task Learning Shengbo Guo, Onno Zoeter, Cédric Archambeau
Online Learning: Stochastic, Constrained, and Smoothed Adversaries Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint Kenji Fukumizu, Gert R. Lanckriet, Bharath K. Sriperumbudur
Sparse Recovery with Brownian Sensing Alexandra Carpentier, Odalric-ambrym Maillard, Rémi Munos
An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments Michael C. Mozer, Benjamin Link, Harold Pashler
Bayesian Bias Mitigation for Crowdsourcing Fabian L. Wauthier, Michael I. Jordan
Ranking annotators for crowdsourced labeling tasks Vikas C. Raykar, Shipeng Yu
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery Scott Niekum, Andrew G. Barto
Probabilistic Joint Image Segmentation and Labeling Adrian Ion, Joao Carreira, Cristian Sminchisescu
Variance Reduction in Monte-Carlo Tree Search Joel Veness, Marc Lanctot, Michael Bowling
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos
An Application of Tree-Structured Expectation Propagation for Channel Decoding Pablo M. Olmos, Luis Salamanca, Juan Fuentes, Fernando Pérez-Cruz
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions Animashree Anandkumar, Vincent Tan, Alan S. Willsky
Structural equations and divisive normalization for energy-dependent component analysis Jun-ichiro Hirayama, Aapo Hyvärinen
Robust Lasso with missing and grossly corrupted observations Nasser M. Nasrabadi, Trac D. Tran, Nam Nguyen
A concave regularization technique for sparse mixture models Martin O. Larsson, Johan Ugander
Learning a Distance Metric from a Network Blake Shaw, Bert Huang, Tony Jebara
Variance Penalizing AdaBoost Pannagadatta K. Shivaswamy, Tony Jebara
Efficient Offline Communication Policies for Factored Multiagent POMDPs João V. Messias, Matthijs Spaan, Pedro U. Lima
Sparse recovery by thresholded non-negative least squares Martin Slawski, Matthias Hein
On Learning Discrete Graphical Models using Greedy Methods Ali Jalali, Christopher C. Johnson, Pradeep K. Ravikumar
Policy Gradient Coagent Networks Philip S. Thomas
Iterative Learning for Reliable Crowdsourcing Systems David R. Karger, Sewoong Oh, Devavrat Shah
A Model for Temporal Dependencies in Event Streams Asela Gunawardana, Christopher Meek, Puyang Xu
Unsupervised learning models of primary cortical receptive fields and receptive field plasticity Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Saxe, Andrew Y. Ng
The Doubly Correlated Nonparametric Topic Model Dae I. Kim, Erik B. Sudderth
MAP Inference for Bayesian Inverse Reinforcement Learning Jaedeug Choi, Kee-eung Kim
Similarity-based Learning via Data Driven Embeddings Purushottam Kar, Prateek Jain
Predicting Dynamic Difficulty Olana Missura, Thomas Gärtner
Sparse Estimation with Structured Dictionaries David P. Wipf
Spectral Methods for Learning Multivariate Latent Tree Structure Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
How biased are maximum entropy models? Jakob H. Macke, Iain Murray, Peter E. Latham
Active learning of neural response functions with Gaussian processes Mijung Park, Greg Horwitz, Jonathan W. Pillow
Priors over Recurrent Continuous Time Processes Ardavan Saeedi, Alexandre Bouchard-côté
Learning to Learn with Compound HD Models Antonio Torralba, Joshua B. Tenenbaum, Ruslan R. Salakhutdinov
Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals Zuoguan Wang, Gerwin Schalk, Qiang Ji
Active Learning with a Drifting Distribution Liu Yang
PiCoDes: Learning a Compact Code for Novel-Category Recognition Alessandro Bergamo, Lorenzo Torresani, Andrew W. Fitzgibbon
Confidence Sets for Network Structure David S. Choi, Patrick J. Wolfe, Edo M. Airoldi
Prismatic Algorithm for Discrete D.C. Programming Problem Yoshinobu Kawahara, Takashi Washio
Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms Liefeng Bo, Xiaofeng Ren, Dieter Fox
Multiclass Boosting: Theory and Algorithms Mohammad J. Saberian, Nuno Vasconcelos
Learning with the weighted trace-norm under arbitrary sampling distributions Rina Foygel, Ohad Shamir, Nati Srebro, Ruslan R. Salakhutdinov
Scalable Training of Mixture Models via Coresets Dan Feldman, Matthew Faulkner, Andreas Krause
Generalised Coupled Tensor Factorisation Kenan Y. Yılmaz, Ali T. Cemgil, Umut Simsekli
Nearest Neighbor based Greedy Coordinate Descent Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
The Fixed Points of Off-Policy TD J. Z. Kolter
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample Gilles Blanchard, Gyemin Lee, Clayton Scott
Trace Lasso: a trace norm regularization for correlated designs Edouard Grave, Guillaume R. Obozinski, Francis R. Bach
Statistical Tests for Optimization Efficiency Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling
Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss Joseph Keshet, David A. McAllester
A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm Julie Dethier, Paul Nuyujukian, Chris Eliasmith, Terrence C. Stewart, Shauki A. Elasaad, Krishna V. Shenoy, Kwabena A. Boahen
Multi-Bandit Best Arm Identification Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric, Sébastien Bubeck
Randomized Algorithms for Comparison-based Search Dominique Tschopp, Suhas Diggavi, Payam Delgosha, Soheil Mohajer
Active Ranking using Pairwise Comparisons Kevin G. Jamieson, Robert Nowak
An Empirical Evaluation of Thompson Sampling Olivier Chapelle, Lihong Li
Blending Autonomous Exploration and Apprenticeship Learning Thomas J. Walsh, Daniel K. Hewlett, Clayton T. Morrison
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation Onur Dikmen, Cédric Févotte
Evaluating the inverse decision-making approach to preference learning Alan Jern, Christopher G. Lucas, Charles Kemp
Sparse Features for PCA-Like Linear Regression Christos Boutsidis, Petros Drineas, Malik Magdon-Ismail
The Manifold Tangent Classifier Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller
Analytical Results for the Error in Filtering of Gaussian Processes Alex K. Susemihl, Ron Meir, Manfred Opper
Improved Algorithms for Linear Stochastic Bandits Yasin Abbasi-yadkori, Dávid Pál, Csaba Szepesvári
Testing a Bayesian Measure of Representativeness Using a Large Image Database Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation Cho-jui Hsieh, Inderjit S. Dhillon, Pradeep K. Ravikumar, Mátyás A. Sustik
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning Michalis K. Titsias, Miguel Lázaro-Gredilla
Practical Variational Inference for Neural Networks Alex Graves
Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability David P. Reichert, Peggy Series, Amos J. Storkey
Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts Matthias Hein, Simon Setzer
Fast and Accurate k-means For Large Datasets Michael Shindler, Alex Wong, Adam W. Meyerson
A rational model of causal inference with continuous causes Thomas L. Griffiths, Michael James
Quasi-Newton Methods for Markov Chain Monte Carlo Yichuan Zhang, Charles A. Sutton
TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning George Konidaris, Scott Niekum, Philip S. Thomas
Speedy Q-Learning Mohammad Ghavamzadeh, Hilbert J. Kappen, Mohammad G. Azar, Rémi Munos
Regularized Laplacian Estimation and Fast Eigenvector Approximation Patrick O. Perry, Michael W. Mahoney
Understanding the Intrinsic Memorability of Images Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning Marius Kloft, Gilles Blanchard
Contextual Gaussian Process Bandit Optimization Andreas Krause, Cheng S. Ong
Co-Training for Domain Adaptation Minmin Chen, Kilian Q. Weinberger, John Blitzer
Autonomous Learning of Action Models for Planning Neville Mehta, Prasad Tadepalli, Alan Fern
Gaussian process modulated renewal processes Yee W. Teh, Vinayak Rao
Linear Submodular Bandits and their Application to Diversified Retrieval Yisong Yue, Carlos Guestrin
Continuous-Time Regression Models for Longitudinal Networks Duy Q. Vu, David Hunter, Padhraic Smyth, Arthur U. Asuncion
On Tracking The Partition Function Guillaume Desjardins, Yoshua Bengio, Aaron C. Courville
Variational Gaussian Process Dynamical Systems Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels Vikas Sindhwani, Aurelie C. Lozano
Selecting Receptive Fields in Deep Networks Adam Coates, Andrew Y. Ng
Convergent Fitted Value Iteration with Linear Function Approximation Daniel J. Lizotte
Algorithms for Hyper-Parameter Optimization James S. Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
Neural Reconstruction with Approximate Message Passing (NeuRAMP) Alyson K. Fletcher, Sundeep Rangan, Lav R. Varshney, Aniruddha Bhargava
Query-Aware MCMC Michael L. Wick, Andrew McCallum
A reinterpretation of the policy oscillation phenomenon in approximate policy iteration Paul Wagner
Inferring spike-timing-dependent plasticity from spike train data Ian Stevenson, Konrad Koerding
Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints Omar Z. Khan, Pascal Poupart, John-mark M. Agosta
A Collaborative Mechanism for Crowdsourcing Prediction Problems Jacob D. Abernethy, Rafael M. Frongillo
Hierarchically Supervised Latent Dirichlet Allocation Adler J. Perotte, Frank Wood, Noemie Elhadad, Nicholas Bartlett
Select and Sample - A Model of Efficient Neural Inference and Learning Jacquelyn A. Shelton, Abdul S. Sheikh, Pietro Berkes, Joerg Bornschein, Joerg Luecke
Selecting the State-Representation in Reinforcement Learning Odalric-ambrym Maillard, Daniil Ryabko, Rémi Munos
Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning Joni K. Pajarinen, Jaakko Peltonen
On the Universality of Online Mirror Descent Nati Srebro, Karthik Sridharan, Ambuj Tewari
Demixed Principal Component Analysis Wieland Brendel, Ranulfo Romo, Christian K. Machens
EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning Feng Yan, Yuan Qi
Hashing Algorithms for Large-Scale Learning Ping Li, Anshumali Shrivastava, Joshua L. Moore, Arnd C. König
Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound Iasonas Kokkinos
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation Nico Goernitz, Christian Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch, Sören Sonnenburg
Predicting response time and error rates in visual search Bo Chen, Vidhya Navalpakkam, Pietro Perona
Kernel Embeddings of Latent Tree Graphical Models Le Song, Eric P. Xing, Ankur P. Parikh
Inference in continuous-time change-point models Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity Po-ling Loh, Martin J. Wainwright
Exploiting spatial overlap to efficiently compute appearance distances between image windows Bogdan Alexe, Viviana Petrescu, Vittorio Ferrari

Accelerated Adaptive Markov Chain for Partition Function Computation Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

Advances in Neural Information Processing Systems 25 (NIPS 2012)

The papers below appear in Advances in Neural Information Processing Systems 25 edited by F. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger.
They are proceedings from the conference, "Neural Information Processing Systems 2012."

Locally Uniform Comparison Image Descriptor Andrew Ziegler, Eric Christiansen, David Kriegman, Serge J. Belongie
Learning from Distributions via Support Measure Machines Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Prof. Bernhard Schölkopf
Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery Ehsan Elhamifar, Guillermo Sapiro, René Vidal
Feature Clustering for Accelerating Parallel Coordinate Descent Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David Haglin
Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA C. M. Niu, Sirish Nandyala, Won J. Sohn, Terence Sanger
Active Learning of Model Evidence Using Bayesian Quadrature Michael Osborne, Roman Garnett, Zoubin Ghahramani, David K. Duvenaud, Stephen J. Roberts, Carl E. Rasmussen
Coupling Nonparametric Mixtures via Latent Dirichlet Processes Dahua Lin, John W. Fisher
Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction Minjie Xu, Jun Zhu, Bo Zhang
Bayesian Hierarchical Reinforcement Learning Feng Cao, Soumya Ray
Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction Christoph H. Lampert
Local Supervised Learning through Space Partitioning Joseph Wang, Venkatesh Saligrama
A Generative Model for Parts-based Object Segmentation S. Eslami, Christopher Williams
Super-Bit Locality-Sensitive Hashing Jianqiu Ji, Jianmin Li, Shuicheng Yan, Bo Zhang, Qi Tian
The Bethe Partition Function of Log-supermodular Graphical Models Nicholas Ruozzi
Random Utility Theory for Social Choice Hossein Azari, David Parks, Lirong Xia
Putting Bayes to sleep Dmitry Adamskiy, Manfred K. Warmuth, Wouter M. Koolen
A new metric on the manifold of kernel matrices with application to matrix geometric means Suvrit Sra
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification Wei Bi, James T. Kwok
Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation Tuo Zhao, Kathryn Roeder, Han Liu
Semiparametric Principal Component Analysis Fang Han, Han Liu
Coding efficiency and detectability of rate fluctuations with non-Poisson neuronal firing Shinsuke Koyama
The representer theorem for Hilbert spaces: a necessary and sufficient condition Francesco Dinuzzo, Prof. Bernhard Schölkopf
On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking Clément Calauzènes, Nicolas Usunier, Patrick Gallinari
Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress Manuel Lopes, Tobias Lang, Marc Toussaint, Pierre-yves Oudeyer
Supervised Learning with Similarity Functions Purushottam Kar, Prateek Jain
Cocktail Party Processing via Structured Prediction Yuxuan Wang, Deliang Wang
Robustness and risk-sensitivity in Markov decision processes Takayuki Osogami
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection Xiaolong Wang, Liang Lin
Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions Alexandra Carpentier, Rémi Munos
Distributed Non-Stochastic Experts Varun Kanade, Zhenming Liu, Bozidar Radunovic
Learning Image Descriptors with the Boosting-Trick Tomasz Trzcinski, Mario Christoudias, Vincent Lepetit, Pascal Fua
Fast Resampling Weighted v-Statistics Chunxiao Zhou, Jiseong Park, Yun Fu
Multi-task Vector Field Learning Binbin Lin, Sen Yang, Chiyuan Zhang, Jieping Ye, Xiaofei He
Memorability of Image Regions Aditya Khosla, Jianxiong Xiao, Antonio Torralba, Aude Oliva
Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions Jaedeug Choi, Kee-eung Kim
Automatic Feature Induction for Stagewise Collaborative Filtering Joonseok Lee, Mingxuan Sun, Seungyeon Kim, Guy Lebanon
Selective Labeling via Error Bound Minimization Quanquan Gu, Tong Zhang, Jiawei Han, Chris H. Ding
Volume Regularization for Binary Classification Koby Crammer, Tal Wagner
Image Denoising and Inpainting with Deep Neural Networks Junyuan Xie, Linli Xu, Enhong Chen
Max-Margin Structured Output Regression for Spatio-Temporal Action Localization Du Tran, Junsong Yuan
Transelliptical Component Analysis Fang Han, Han Liu
Action-Model Based Multi-agent Plan Recognition Hankz H. Zhuo, Qiang Yang, Subbarao Kambhampati
Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity Angela Eigenstetter, Bjorn Ommer
Non-parametric Approximate Dynamic Programming via the Kernel Method Nikhil Bhat, Vivek Farias, Ciamac C. Moallemi
Optimal Regularized Dual Averaging Methods for Stochastic Optimization Xi Chen, Qihang Lin, Javier Pena
The variational hierarchical EM algorithm for clustering hidden Markov models Emanuele Coviello, Gert R. Lanckriet, Antoni B. Chan
Truncation-free Online Variational Inference for Bayesian Nonparametric Models Chong Wang, David M. Blei
3D Social Saliency from Head-mounted Cameras Hyun S. Park, Eakta Jain, Yaser Sheikh
Context-Sensitive Decision Forests for Object Detection Peter Kontschieder, Samuel R. Bulò, Antonio Criminisi, Pushmeet Kohli, Marcello Pelillo, Horst Bischof
Learning Invariant Representations of Molecules for Atomization Energy Prediction Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, Anatole V. Lilienfeld, Klaus-Robert Müller
Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button Joan Fruitet, Alexandra Carpentier, Maureen Clerc, Rémi Munos
Multiplicative Forests for Continuous-Time Processes Jeremy Weiss, Sriraam Natarajan, David Page
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task Jenna Wiens, Eric Horvitz, John V. Guttag
Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison Tianbao Yang, Yu-feng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou
Multiclass Learning Approaches: A Theoretical Comparison with Implications Amit Daniely, Sivan Sabato, Shai S. Shwartz
Stochastic Gradient Descent with Only One Projection Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jinfeng Yi
Neuronal Spike Generation Mechanism as an Oversampling, Noise-shaping A-to-D converter Dmitri B. Chklovskii, Daniel Soudry
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction Pietro D. Lena, Ken Nagata, Pierre F. Baldi
Assessing Blinding in Clinical Trials Ognjen Arandjelovic
Scalable nonconvex inexact proximal splitting Suvrit Sra
Learning to Discover Social Circles in Ego Networks Jure Leskovec, Julian J. Mcauley
A Conditional Multinomial Mixture Model for Superset Label Learning Liping Liu, Thomas G. Dietterich
Majorization for CRFs and Latent Likelihoods Tony Jebara, Anna Choromanska
Ensemble weighted kernel estimators for multivariate entropy estimation Kumar Sricharan, Alfred O. Hero
Efficient high dimensional maximum entropy modeling via symmetric partition functions Paul Vernaza, Drew Bagnell
Discriminatively Trained Sparse Code Gradients for Contour Detection Ren Xiaofeng, Liefeng Bo
Analyzing 3D Objects in Cluttered Images Mohsen Hejrati, Deva Ramanan
Nonconvex Penalization Using Laplace Exponents and Concave Conjugates Zhihua Zhang, Bojun Tu
3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model Sanja Fidler, Sven Dickinson, Raquel Urtasun
Structured Learning of Gaussian Graphical Models Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-in Lee, Maryam Fazel
A Polylog Pivot Steps Simplex Algorithm for Classification Elad Hazan, Zohar Karnin
Shifting Weights: Adapting Object Detectors from Image to Video Kevin Tang, Vignesh Ramanathan, Li Fei-fei, Daphne Koller
A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound Shusen Wang, Zhihua Zhang
Convolutional-Recursive Deep Learning for 3D Object Classification Richard Socher, Brody Huval, Bharath Bath, Christopher D. Manning, Andrew Y. Ng
Semi-Supervised Domain Adaptation with Non-Parametric Copulas David Lopez-paz, Jose M. Hernández-lobato, Prof. Bernhard Schölkopf
Identification of Recurrent Patterns in the Activation of Brain Networks Firdaus Janoos, Weichang Li, Niranjan Subrahmanya, Istvan Morocz, William Wells
Density-Difference Estimation Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus D. Plessis, Song Liu, Ichiro Takeuchi
Variational Inference for Crowdsourcing Qiang Liu, Jian Peng, Alexander T. Ihler
MCMC for continuous-time discrete-state systems Vinayak Rao, Yee W. Teh
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling Pieter-jan Kindermans, Hannes Verschore, David Verstraeten, Benjamin Schrauwen
Learning about Canonical Views from Internet Image Collections Elad Mezuman, Yair Weiss
Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data Assaf Glazer, Michael Lindenbaum, Shaul Markovitch
Multiresolution Gaussian Processes Emily Fox, David B. Dunson
Localizing 3D cuboids in single-view images Jianxiong Xiao, Bryan Russell, Antonio Torralba
Newton-Like Methods for Sparse Inverse Covariance Estimation Figen Oztoprak, Jorge Nocedal, Steven Rennie, Peder A. Olsen
Learning to Align from Scratch Gary Huang, Marwan Mattar, Honglak Lee, Erik G. Learned-miller
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints Stefan Habenschuss, Johannes Bill, Bernhard Nessler
Clustering Aggregation as Maximum-Weight Independent Set Nan Li, Longin J. Latecki
Topology Constraints in Graphical Models Marcelo Fiori, Pablo Musé, Guillermo Sapiro
Transelliptical Graphical Models Han Liu, Fang Han, Cun-hui Zhang
Kernel Latent SVM for Visual Recognition Weilong Yang, Yang Wang, Arash Vahdat, Greg Mori
Learning Partially Observable Models Using Temporally Abstract Decision Trees Erik Talvitie
Proximal Newton-type methods for convex optimization Jason D. Lee, Yuekai Sun, Michael Saunders
Regularized Off-Policy TD-Learning Bo Liu, Sridhar Mahadevan, Ji Liu
Multi-criteria Anomaly Detection using Pareto Depth Analysis Ko-jen Hsiao, Kevin Xu, Jeff Calder, Alfred O. Hero
Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes Jake Bouvrie, Jean-jeacques Slotine
Calibrated Elastic Regularization in Matrix Completion Tingni Sun, Cun-hui Zhang
Predicting Action Content On-Line and in Real Time before Action Onset – an Intracranial Human Study Uri Maoz, Shengxuan Ye, Ian Ross, Adam Mamelak, Christof Koch
Searching for objects driven by context Bogdan Alexe, Nicolas Heess, Yee W. Teh, Vittorio Ferrari
Timely Object Recognition Sergey Karayev, Tobias Baumgartner, Mario Fritz, Trevor Darrell
Nonparanormal Belief Propagation (NPNBP) Gal Elidan, Cobi Cario
Deep Representations and Codes for Image Auto-Annotation Ryan Kiros, Csaba Szepesvári
A Spectral Algorithm for Latent Dirichlet Allocation Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-kai Liu
Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs Aharon Birnbaum, Shai S. Shwartz
Matrix reconstruction with the local max norm Rina Foygel, Nathan Srebro, Ruslan R. Salakhutdinov
Analog readout for optical reservoir computers Anteo Smerieri, François Duport, Yvon Paquot, Benjamin Schrauwen, Marc Haelterman, Serge Massar
Accuracy at the Top Stephen Boyd, Corinna Cortes, Mehryar Mohri, Ana Radovanovic
Minimizing Sparse High-Order Energies by Submodular Vertex-Cover Andrew Delong, Olga Veksler, Anton Osokin, Yuri Boykov
Perfect Dimensionality Recovery by Variational Bayesian PCA Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. D. Babacan
Mirror Descent Meets Fixed Share (and feels no regret) Nicolò Cesa-bianchi, Pierre Gaillard, Gabor Lugosi, Gilles Stoltz
Near-optimal Differentially Private Principal Components Kamalika Chaudhuri, Anand Sarwate, Kaushik Sinha
Random function priors for exchangeable arrays with applications to graphs and relational data James Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy
Inverse Reinforcement Learning through Structured Classification Edouard Klein, Matthieu Geist, Bilal Piot, Olivier Pietquin
Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics Ashwini Shukla, Aude Billard
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search Arthur Guez, David Silver, Peter Dayan
Dimensionality Dependent PAC-Bayes Margin Bound Chi Jin, Liwei Wang
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs Anima Anandkumar, Ragupathyraj Valluvan
Learning Mixtures of Tree Graphical Models Anima Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade
Hamming Distance Metric Learning Mohammad Norouzi, David J. Fleet, Ruslan R. Salakhutdinov
Spiking and saturating dendrites differentially expand single neuron computation capacity Romain Cazé, Mark Humphries, Boris S. Gutkin
Clustering by Nonnegative Matrix Factorization Using Graph Random Walk Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen, Erkki Oja
Delay Compensation with Dynamical Synapses Chi Fung, K. Wong, Si Wu
ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
Recognizing Activities by Attribute Dynamics Weixin Li, Nuno Vasconcelos
Compressive Sensing MRI with Wavelet Tree Sparsity Chen Chen, Junzhou Huang
Training sparse natural image models with a fast Gibbs sampler of an extended state space Lucas Theis, Jascha Sohl-dickstein, Matthias Bethge
A Bayesian Approach for Policy Learning from Trajectory Preference Queries Aaron Wilson, Alan Fern, Prasad Tadepalli
GenDeR: A Generic Diversified Ranking Algorithm Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw Szymanski
On Multilabel Classification and Ranking with Partial Feedback Claudio Gentile, Francesco Orabona
The Lovász ϑ function, SVMs and finding large dense subgraphs Vinay Jethava, Anders Martinsson, Chiranjib Bhattacharyya, Devdatt Dubhashi
Multi-Task Averaging Sergey Feldman, Maya Gupta, Bela Frigyik
Unsupervised Structure Discovery for Semantic Analysis of Audio Sourish Chaudhuri, Bhiksha Raj
A Marginalized Particle Gaussian Process Regression Yali Wang, Brahim Chaib-draa
Angular Quantization-based Binary Codes for Fast Similarity Search Yunchao Gong, Sanjiv Kumar, Vishal Verma, Svetlana Lazebnik
Optimal kernel choice for large-scale two-sample tests Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, Bharath K. Sriperumbudur
Factoring nonnegative matrices with linear programs Ben Recht, Christopher Re, Joel Tropp, Victor Bittorf
Large Scale Distributed Deep Networks Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le, Andrew Y. Ng
Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-yan Liu
Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination Won H. Kim, Deepti Pachauri, Charles Hatt, Moo. K. Chung, Sterling Johnson, Vikas Singh
A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation Aaron Defazio, Tibério S. Caetano
Fused sparsity and robust estimation for linear models with unknown variance Arnak Dalalyan, Yin Chen
How Prior Probability Influences Decision Making: A Unifying Probabilistic Model Yanping Huang, Timothy Hanks, Mike Shadlen, Abram L. Friesen, Rajesh P. Rao
High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction Hua Wang, Feiping Nie, Heng Huang, Jingwen Yan, Sungeun Kim, Shannon Risacher, Andrew Saykin, Li Shen
Symmetric Correspondence Topic Models for Multilingual Text Analysis Kosuke Fukumasu, Koji Eguchi, Eric P. Xing
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data Michael C. Hughes, Emily Fox, Erik B. Sudderth
Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference Xue-xin Wei, Alan A. Stocker
Efficient Sampling for Bipartite Matching Problems Maksims Volkovs, Richard S. Zemel
Learning visual motion in recurrent neural networks Marius Pachitariu, Maneesh Sahani
Learned Prioritization for Trading Off Accuracy and Speed Jiarong Jiang, Adam Teichert, Jason Eisner, Hal Daume
Value Pursuit Iteration Amir M. Farahmand, Doina Precup
Compressive neural representation of sparse, high-dimensional probabilities Xaq Pitkow
Graphical Models via Generalized Linear Models Eunho Yang, Genevera Allen, Zhandong Liu, Pradeep K. Ravikumar
CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem Henrik Ohlsson, Allen Yang, Roy Dong, Shankar Sastry
Co-Regularized Hashing for Multimodal Data Yi Zhen, Dit-Yan Yeung
Convergence and Energy Landscape for Cheeger Cut Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James V. Brecht
Symbolic Dynamic Programming for Continuous State and Observation POMDPs Zahra Zamani, Scott Sanner, Pascal Poupart, Kristian Kersting
Bayesian Probabilistic Co-Subspace Addition Lei Shi
Scaled Gradients on Grassmann Manifolds for Matrix Completion Thanh Ngo, Yousef Saad
Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging Chris Hinrichs, Vikas Singh, Jiming Peng, Sterling Johnson
Privacy Aware Learning Martin J. Wainwright, Michael I. Jordan, John C. Duchi
Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods Andre Wibisono, Martin J. Wainwright, Michael I. Jordan, John C. Duchi
Hierarchical Optimistic Region Selection driven by Curiosity Odalric-ambrym Maillard
Sparse Prediction with the k-Support Norm Andreas Argyriou, Rina Foygel, Nathan Srebro
Active Learning of Multi-Index Function Models Tyagi Hemant, Volkan Cevher
Learning Multiple Tasks using Shared Hypotheses Koby Crammer, Yishay Mansour
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization Doina Precup, Joelle Pineau, Andre S. Barreto
Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models Kei Wakabayashi, Takao Miura
Communication-Efficient Algorithms for Statistical Optimization Yuchen Zhang, Martin J. Wainwright, John C. Duchi
Identifiability and Unmixing of Latent Parse Trees Daniel J. Hsu, Sham M. Kakade, Percy S. Liang
Bayesian nonparametric models for ranked data Francois Caron, Yee W. Teh
Feature-aware Label Space Dimension Reduction for Multi-label Classification Yao-nan Chen, Hsuan-tien Lin
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions Alekh Agarwal, Sahand Negahban, Martin J. Wainwright
Graphical Gaussian Vector for Image Categorization Tatsuya Harada, Yasuo Kuniyoshi
Joint Modeling of a Matrix with Associated Text via Latent Binary Features Xianxing Zhang, Lawrence Carin
Proper losses for learning from partial labels Jesús Cid-sueiro
Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation Benjamin Rolfs, Bala Rajaratnam, Dominique Guillot, Ian Wong, Arian Maleki
Selecting Diverse Features via Spectral Regularization Abhimanyu Das, Anirban Dasgupta, Ravi Kumar
Monte Carlo Methods for Maximum Margin Supervised Topic Models Qixia Jiang, Jun Zhu, Maosong Sun, Eric P. Xing
Parametric Local Metric Learning for Nearest Neighbor Classification Jun Wang, Alexandros Kalousis, Adam Woznica
A Linear Time Active Learning Algorithm for Link Classification Nicolò Cesa-bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella
Bayesian Warped Gaussian Processes Miguel Lázaro-Gredilla
Nonparametric Reduced Rank Regression Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty
Multiresolution analysis on the symmetric group Risi Kondor, Walter Dempsey
Isotropic Hashing Weihao Kong, Wu-jun Li
On Lifting the Gibbs Sampling Algorithm Deepak Venugopal, Vibhav Gogate
On the connections between saliency and tracking Vijay Mahadevan, Nuno Vasconcelos
Convex Multi-view Subspace Learning Martha White, Xinhua Zhang, Dale Schuurmans, Yao-liang Yu
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data Lars Buesing, Jakob H. Macke, Maneesh Sahani
Mixability in Statistical Learning Tim V. Erven, Peter Grünwald, Mark D. Reid, Robert C. Williamson
Waveform Driven Plasticity in BiFeO3 Memristive Devices: Model and Implementation Christian Mayr, Paul Stärke, Johannes Partzsch, Love Cederstroem, Rene Schüffny, Yao Shuai, Nan Du, Heidemarie Schmidt
A lattice filter model of the visual pathway Karol Gregor, Dmitri B. Chklovskii
Semantic Kernel Forests from Multiple Taxonomies Sung Ju Hwang, Kristen Grauman, Fei Sha
Causal discovery with scale-mixture model for spatiotemporal variance dependencies Zhitang Chen, Kun Zhang, Laiwan Chan
Natural Images, Gaussian Mixtures and Dead Leaves Daniel Zoran, Yair Weiss
Dual-Space Analysis of the Sparse Linear Model Yi Wu, David P. Wipf
Active Comparison of Prediction Models Christoph Sawade, Niels Landwehr, Tobias Scheffer
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning Ronald Ortner, Daniil Ryabko
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning Jinfeng Yi, Rong Jin, Shaili Jain, Tianbao Yang, Anil K. Jain
Learning curves for multi-task Gaussian process regression Peter Sollich, Simon Ashton
Kernel Hyperalignment Alexander Lorbert, Peter J. Ramadge
Multiple Choice Learning: Learning to Produce Multiple Structured Outputs Abner Guzmán-rivera, Dhruv Batra, Pushmeet Kohli
Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions Mathieu Sinn, Bei Chen
Persistent Homology for Learning Densities with Bounded Support Florian T. Pokorny, Hedvig Kjellström, Danica Kragic, Carl Ek
On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes Bruno Scherrer, Boris Lesner
Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model Sander M. Bohte
MAP Inference in Chains using Column Generation David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum
Bayesian Nonparametric Modeling of Suicide Attempts Francisco Ruiz, Isabel Valera, Carlos Blanco, Fernando Pérez-Cruz
Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling Antonino Freno, Mikaela Keller, Marc Tommasi
Neurally Plausible Reinforcement Learning of Working Memory Tasks Jaldert Rombouts, Pieter Roelfsema, Sander M. Bohte
Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions Neil Burch, Marc Lanctot, Duane Szafron, Richard G. Gibson
Repulsive Mixtures Francesca Petralia, Vinayak Rao, David B. Dunson
Fully Bayesian inference for neural models with negative-binomial spiking James Scott, Jonathan W. Pillow
Slice Normalized Dynamic Markov Logic Networks Tivadar Papai, Henry Kautz, Daniel Stefankovic
Meta-Gaussian Information Bottleneck Melanie Rey, Volker Roth
Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification Yung-kyun Noh, Frank Park, Daniel D. Lee
The Perturbed Variation Maayan Harel, Shie Mannor
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization Konstantinos Tsianos, Sean Lawlor, Michael G. Rabbat
The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes Simon Lyons, Amos J. Storkey, Simo Särkkä
Online allocation and homogeneous partitioning for piecewise constant mean-approximation Alexandra Carpentier, Odalric-ambrym Maillard
Learning as MAP Inference in Discrete Graphical Models Xianghang Liu, James Petterson, Tibério S. Caetano
A mechanistic model of early sensory processing based on subtracting sparse representations Shaul Druckmann, Tao Hu, Dmitri B. Chklovskii
Multi-Stage Multi-Task Feature Learning Pinghua Gong, Jieping Ye, Chang-shui Zhang
From Deformations to Parts: Motion-based Segmentation of 3D Objects Soumya Ghosh, Matthew Loper, Erik B. Sudderth, Michael J. Black
Phoneme Classification using Constrained Variational Gaussian Process Dynamical System Hyunsin Park, Sungrack Yun, Sanghyuk Park, Jongmin Kim, Chang D. Yoo
Bayesian estimation of discrete entropy with mixtures of stick-breaking priors Evan Archer, Il Memming Park, Jonathan W. Pillow
A Geometric take on Metric Learning Søren Hauberg, Oren Freifeld, Michael J. Black
Learning the Architecture of Sum-Product Networks Using Clustering on Variables Aaron Dennis, Dan Ventura
Pointwise Tracking the Optimal Regression Function Yair Wiener, Ran El-Yaniv
Bayesian nonparametric models for bipartite graphs Francois Caron
Reducing statistical time-series problems to binary classification Daniil Ryabko, Jeremie Mary
Tractable Objectives for Robust Policy Optimization Katherine Chen, Michael Bowling
Classification Calibration Dimension for General Multiclass Losses Harish G. Ramaswamy, Shivani Agarwal
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses Po-ling Loh, Martin J. Wainwright
Collaborative Gaussian Processes for Preference Learning Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, Jose M. Hernández-lobato
Approximating Concavely Parameterized Optimization Problems Joachim Giesen, Jens Mueller, Soeren Laue, Sascha Swiercy
Gradient-based kernel method for feature extraction and variable selection Kenji Fukumizu, Chenlei Leng
Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making Pradeep Shenoy, Angela J. Yu
On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks Qirong Ho, Junming Yin, Eric P. Xing
Relax and Randomize : From Value to Algorithms Sasha Rakhlin, Ohad Shamir, Karthik Sridharan
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL Nishant Mehta, Dongryeol Lee, Alexander G. Gray
Spectral Learning of General Weighted Automata via Constrained Matrix Completion Borja Balle, Mehryar Mohri
Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum L_p Loss Zhuo Wang, Alan A. Stocker, Daniel D. Lee
Algorithms for Learning Markov Field Policies Abdeslam Boularias, Jan R. Peters, Oliver B. Kroemer
Affine Independent Variational Inference Edward Challis, David Barber
Learning from the Wisdom of Crowds by Minimax Entropy Denny Zhou, Sumit Basu, Yi Mao, John C. Platt
Clustering Sparse Graphs Yudong Chen, Sujay Sanghavi, Huan Xu
Sketch-Based Linear Value Function Approximation Marc Bellemare, Joel Veness, Michael Bowling
Multimodal Learning with Deep Boltzmann Machines Nitish Srivastava, Ruslan R. Salakhutdinov
Learning with Target Prior Zuoguan Wang, Siwei Lyu, Gerwin Schalk, Qiang Ji
Slice sampling normalized kernel-weighted completely random measure mixture models Nicholas Foti, Sinead Williamson
Scalable Inference of Overlapping Communities Prem K. Gopalan, Sean Gerrish, Michael Freedman, David M. Blei, David M. Mimno
Online L1-Dictionary Learning with Application to Novel Document Detection Shiva P. Kasiviswanathan, Huahua Wang, Arindam Banerjee, Prem Melville
A systematic approach to extracting semantic information from functional MRI data Francisco Pereira, Matthew Botvinick
Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding Philip Sterne, Joerg Bornschein, Abdul-saboor Sheikh, Joerg Luecke, Jacquelyn A. Shelton
Learning optimal spike-based representations Ralph Bourdoukan, David Barrett, Sophie Deneve, Christian K. Machens
Collaborative Ranking With 17 Parameters Maksims Volkovs, Richard S. Zemel
Rational inference of relative preferences Nisheeth Srivastava, Paul R. Schrater
The topographic unsupervised learning of natural sounds in the auditory cortex Hiroki Terashima, Masato Okada
Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand Amy Greenwald, Jiacui Li, Eric Sodomka
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation Cho-jui Hsieh, Arindam Banerjee, Inderjit S. Dhillon, Pradeep K. Ravikumar
A Simple and Practical Algorithm for Differentially Private Data Release Moritz Hardt, Katrina Ligett, Frank Mcsherry
Bayesian active learning with localized priors for fast receptive field characterization Mijung Park, Jonathan W. Pillow
Weighted Likelihood Policy Search with Model Selection Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara
Learning the Dependency Structure of Latent Factors Yunlong He, Yanjun Qi, Koray Kavukcuoglu, Haesun Park
Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva
Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins Alex Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun
Dip-means: an incremental clustering method for estimating the number of clusters Argyris Kalogeratos, Aristidis Likas
No-Regret Algorithms for Unconstrained Online Convex Optimization Brendan Mcmahan, Matthew Streeter
Bayesian models for Large-scale Hierarchical Classification Siddharth Gopal, Yiming Yang, Bing Bai, Alexandru Niculescu-mizil
Recovery of Sparse Probability Measures via Convex Programming Mert Pilanci, Laurent E. Ghaoui, Venkat Chandrasekaran
Multiple Operator-valued Kernel Learning Hachem Kadri, Alain Rakotomamonjy, Philippe Preux, Francis R. Bach
Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning Ulugbek Kamilov, Sundeep Rangan, Michael Unser, Alyson K. Fletcher
A Better Way to Pretrain Deep Boltzmann Machines Geoffrey E. Hinton, Ruslan R. Salakhutdinov
Towards a learning-theoretic analysis of spike-timing dependent plasticity David Balduzzi, Michel Besserve
Learning Manifolds with K-Means and K-Flats Guillermo Canas, Tomaso Poggio, Lorenzo Rosasco
Iterative ranking from pair-wise comparisons Sahand Negahban, Sewoong Oh, Devavrat Shah
A Polynomial-time Form of Robust Regression Yao-liang Yu, Özlem Aslan, Dale Schuurmans
Learning Probability Measures with respect to Optimal Transport Metrics Guillermo Canas, Lorenzo Rosasco
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker
Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting Amadou Ba, Mathieu Sinn, Yannig Goude, Pascal Pompey
Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs Michael Collins, Shay B. Cohen
Semi-supervised Eigenvectors for Locally-biased Learning Toke Hansen, Michael W. Mahoney
Exponential Concentration for Mutual Information Estimation with Application to Forests Han Liu, Larry Wasserman, John D. Lafferty
Augment-and-Conquer Negative Binomial Processes Mingyuan Zhou, Lawrence Carin
Transferring Expectations in Model-based Reinforcement Learning Trung Nguyen, Tomi Silander, Tze Y. Leong
Minimization of Continuous Bethe Approximations: A Positive Variation Jason Pacheco, Erik B. Sudderth
Non-linear Metric Learning Dor Kedem, Stephen Tyree, Fei Sha, Gert R. Lanckriet, Kilian Q. Weinberger
Factorial LDA: Sparse Multi-Dimensional Text Models Michael Paul, Mark Dredze
Ancestor Sampling for Particle Gibbs Fredrik Lindsten, Thomas Schön, Michael I. Jordan
Modelling Reciprocating Relationships with Hawkes Processes Charles Blundell, Jeff Beck, Katherine A. Heller
Expectation Propagation in Gaussian Process Dynamical Systems Marc Deisenroth, Shakir Mohamed
A quasi-Newton proximal splitting method Stephen Becker, Jalal Fadili
Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization Demba Ba, Behtash Babadi, Patrick Purdon, Emery Brown
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems Morteza Ibrahimi, Adel Javanmard, Benjamin V. Roy
Multilabel Classification using Bayesian Compressed Sensing Ashish Kapoor, Raajay Viswanathan, Prateek Jain
Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization Stephen Bach, Matthias Broecheler, Lise Getoor, Dianne O'leary
A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets Nicolas L. Roux, Mark Schmidt, Francis R. Bach
Query Complexity of Derivative-Free Optimization Kevin G. Jamieson, Robert Nowak, Ben Recht
Emergence of Object-Selective Features in Unsupervised Feature Learning Adam Coates, Andrej Karpathy, Andrew Y. Ng
Burn-in, bias, and the rationality of anchoring Falk Lieder, Thomas Griffiths, Noah Goodman
Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes Michael Bryant, Erik B. Sudderth
A Neural Autoregressive Topic Model Hugo Larochelle, Stanislas Lauly
A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes Thomas Furmston, David Barber
Entangled Monte Carlo Seong-hwan Jun, Liangliang Wang, Alexandre Bouchard-côté
Near-Optimal MAP Inference for Determinantal Point Processes Jennifer Gillenwater, Alex Kulesza, Ben Taskar
Probabilistic Low-Rank Subspace Clustering S. D. Babacan, Shinichi Nakajima, Minh Do
How They Vote: Issue-Adjusted Models of Legislative Behavior Sean Gerrish, David M. Blei
Density Propagation and Improved Bounds on the Partition Function Stefano Ermon, Ashish Sabharwal, Bart Selman, Carla P. Gomes
Perceptron Learning of SAT Alex Flint, Matthew Blaschko
Learning Networks of Heterogeneous Influence Nan Du, Le Song, Ming Yuan, Alex J. Smola
Multiclass Learning with Simplex Coding Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco, Jean-jeacques Slotine
FastEx: Hash Clustering with Exponential Families Amr Ahmed, Sujith Ravi, Alex J. Smola, Shravan M. Narayanamurthy
Topic-Partitioned Multinetwork Embeddings Peter Krafft, Juston Moore, Bruce Desmarais, Hanna M. Wallach
Learning Label Trees for Probabilistic Modelling of Implicit Feedback Andriy Mnih, Yee W. Teh
Learning with Recursive Perceptual Representations Oriol Vinyals, Yangqing Jia, Li Deng, Trevor Darrell
Link Prediction in Graphs with Autoregressive Features Emile Richard, Stephane Gaiffas, Nicolas Vayatis
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images Dan Ciresan, Alessandro Giusti, Luca M. Gambardella, Juergen Schmidhuber
Scalable imputation of genetic data with a discrete fragmentation-coagulation process Lloyd Elliott, Yee W. Teh
Gradient Weights help Nonparametric Regressors Samory Kpotufe, Abdeslam Boularias
Online Sum-Product Computation Over Trees Mark Herbster, Stephen Pasteris, Fabio Vitale
Sparse Approximate Manifolds for Differential Geometric MCMC Ben Calderhead, Mátyás A. Sustik
Fast Variational Inference in the Conjugate Exponential Family James Hensman, Magnus Rattray, Neil D. Lawrence
Bayesian Pedigree Analysis using Measure Factorization Bonnie Kirkpatrick, Alexandre Bouchard-côté
Accelerated Training for Matrix-norm Regularization: A Boosting Approach Xinhua Zhang, Dale Schuurmans, Yao-liang Yu
Controlled Recognition Bounds for Visual Learning and Exploration Vasiliy Karasev, Alessandro Chiuso, Stefano Soatto
Distributed Probabilistic Learning for Camera Networks with Missing Data Sejong Yoon, Vladimir Pavlovic
Submodular-Bregman and the Lovász-Bregman Divergences with Applications Rishabh Iyer, Jeff A. Bilmes
Minimizing Uncertainty in Pipelines Nilesh Dalvi, Aditya Parameswaran, Vibhor Rastogi
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan P. Adams
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach Dijun Luo, Heng Huang, Feiping Nie, Chris H. Ding
The Time-Marginalized Coalescent Prior for Hierarchical Clustering Levi Boyles, Max Welling
Fusion with Diffusion for Robust Visual Tracking Yu Zhou, Xiang Bai, Wenyu Liu, Longin J. Latecki
A nonparametric variable clustering model Konstantina Palla, Zoubin Ghahramani, David A. Knowles
Priors for Diversity in Generative Latent Variable Models James T. Kwok, Ryan P. Adams
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function Pedro Ortega, Jordi Grau-moya, Tim Genewein, David Balduzzi, Daniel Braun
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms Ofer Meshi, Amir Globerson, Tommi S. Jaakkola
Projection Retrieval for Classification Madalina Fiterau, Artur Dubrawski
Hierarchical spike coding of sound Yan Karklin, Chaitanya Ekanadham, Eero P. Simoncelli
Human memory search as a random walk in a semantic network Joseph L. Austerweil, Joshua T. Abbott, Thomas L. Griffiths
Probabilistic n-Choose-k Models for Classification and Ranking Kevin Swersky, Brendan J. Frey, Daniel Tarlow, Richard S. Zemel, Ryan P. Adams
Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models Jeff Beck, Alexandre Pouget, Katherine A. Heller
Cost-Sensitive Exploration in Bayesian Reinforcement Learning Dongho Kim, Kee-eung Kim, Pascal Poupart
Learning with Partially Absorbing Random Walks Xiao-ming Wu, Zhenguo Li, Anthony M. So, John Wright, Shih-fu Chang
Locating Changes in Highly Dependent Data with Unknown Number of Change Points Azadeh Khaleghi, Daniil Ryabko
Probabilistic Event Cascades for Alzheimer's disease Jonathan Huang, Daniel Alexander
Efficient and direct estimation of a neural subunit model for sensory coding Brett Vintch, Andrew Zaharia, J Movshon, Eero P. Simoncelli
One Permutation Hashing Ping Li, Art Owen, Cun-hui Zhang
Unsupervised Template Learning for Fine-Grained Object Recognition Shulin Yang, Liefeng Bo, Jue Wang, Linda G. Shapiro
Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds Teodor M. Moldovan, Pieter Abbeel
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy
Imitation Learning by Coaching He He, Jason Eisner, Hal Daume
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models Ke Jiang, Brian Kulis, Michael I. Jordan
A latent factor model for highly multi-relational data Rodolphe Jenatton, Nicolas L. Roux, Antoine Bordes, Guillaume R. Obozinski
Entropy Estimations Using Correlated Symmetric Stable Random Projections Ping Li, Cun-hui Zhang
Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression Piyush Rai, Abhishek Kumar, Hal Daume
Continuous Relaxations for Discrete Hamiltonian Monte Carlo Yichuan Zhang, Zoubin Ghahramani, Amos J. Storkey, Charles A. Sutton
Deep Learning of Invariant Features via Simulated Fixations in Video Will Zou, Shenghuo Zhu, Kai Yu, Andrew Y. Ng
Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric
On the Sample Complexity of Robust PCA Matthew Coudron, Gilad Lerman
Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning Matthew Der, Lawrence K. Saul
Discriminative Learning of Sum-Product Networks Robert Gens, Pedro Domingos
Trajectory-Based Short-Sighted Probabilistic Planning Felipe Trevizan, Manuela Veloso
Tight Bounds on Profile Redundancy and Distinguishability Jayadev Acharya, Hirakendu Das, Alon Orlitsky
Interpreting prediction markets: a stochastic approach Rafael M. Frongillo, Nicholás Della Penna, Mark D. Reid
Risk-Aversion in Multi-armed Bandits Amir Sani, Alessandro Lazaric, Rémi Munos
Confusion-Based Online Learning and a Passive-Aggressive Scheme Liva Ralaivola
Cardinality Restricted Boltzmann Machines Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S. Zemel, Ruslan R. Salakhutdinov, Ryan P. Adams
Generalization Bounds for Domain Adaptation Chao Zhang, Lei Zhang, Jieping Ye

Advances in Neural Information Processing Systems 26 (NIPS 2013)

The papers below appear in Advances in Neural Information Processing Systems 26 edited by C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger.
They are proceedings from the conference, "Neural Information Processing Systems 2013."

The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Prof. Bernhard Schölkopf
Documents as multiple overlapping windows into grids of counts Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski
Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively Wen-Hao Zhang, Si Wu
Latent Maximum Margin Clustering Guang-Tong Zhou, Tian Lan, Arash Vahdat, Greg Mori
Data-driven Distributionally Robust Polynomial Optimization Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu
Transfer Learning in a Transductive Setting Marcus Rohrbach, Sandra Ebert, Bernt Schiele
Bayesian optimization explains human active search Ali Borji, Laurent Itti
Provable Subspace Clustering: When LRR meets SSC Yu-Xiang Wang, Huan Xu, Chenlei Leng
Generalized Random Utility Models with Multiple Types Hossein Azari Soufiani, Hansheng Diao, Zhenyu Lai, David C. Parkes
Polar Operators for Structured Sparse Estimation Xinhua Zhang, Yao-Liang Yu, Dale Schuurmans
On Decomposing the Proximal Map Yao-Liang Yu
Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs Liam C. MacDermed, Charles Isbell
PAC-Bayes-Empirical-Bernstein Inequality Ilya O. Tolstikhin, Yevgeny Seldin
Modeling Clutter Perception using Parametric Proto-object Partitioning Chen-Ping Yu, Wen-Yu Hua, Dimitris Samaras, Greg Zelinsky
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching Marcelo Fiori, Pablo Sprechmann, Joshua Vogelstein, Pablo Muse, Guillermo Sapiro
Transportability from Multiple Environments with Limited Experiments Elias Bareinboim, Sanghack Lee, Vasant Honavar, Judea Pearl
More data speeds up training time in learning halfspaces over sparse vectors Amit Daniely, Nati Linial, Shai Shalev-Shwartz
Causal Inference on Time Series using Restricted Structural Equation Models Jonas Peters, Dominik Janzing, Prof. Bernhard Schölkopf
Deep Fisher Networks for Large-Scale Image Classification Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
Sparse Additive Text Models with Low Rank Background Lei Shi
Variance Reduction for Stochastic Gradient Optimization Chong Wang, Xi Chen, Alexander J. Smola, Eric P. Xing
Training and Analysing Deep Recurrent Neural Networks Michiel Hermans, Benjamin Schrauwen
A simple example of Dirichlet process mixture inconsistency for the number of components Jeffrey W. Miller, Matthew T. Harrison
Variational Policy Search via Trajectory Optimization Sergey Levine, Vladlen Koltun
Scalable kernels for graphs with continuous attributes Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt
Density estimation from unweighted k-nearest neighbor graphs: a roadmap Ulrike Von Luxburg, Morteza Alamgir
Decision Jungles: Compact and Rich Models for Classification Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach Zhenwen Dai, Georgios Exarchakis, Jörg Lücke
Actor-Critic Algorithms for Risk-Sensitive MDPs Prashanth L.A., Mohammad Ghavamzadeh
Summary Statistics for Partitionings and Feature Allocations Isik B. Fidaner, Taylan Cemgil
One-shot learning and big data with n=2 Lee H. Dicker, Dean P. Foster
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression Michalis Titsias RC AUEB, Miguel Lazaro-Gredilla
Correlations strike back (again): the case of associative memory retrieval Cristina Savin, Peter Dayan, Mate Lengyel
Optimal Neural Population Codes for High-dimensional Stimulus Variables Zhuo Wang, Alan A. Stocker, Daniel D. Lee
Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking Ryan D. Turner, Steven Bottone, Clay J. Stanek
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction Rie Johnson, Tong Zhang
Using multiple samples to learn mixture models Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition Tzu-Kuo Huang, Jeff Schneider
On model selection consistency of penalized M-estimators: a geometric theory Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
Dropout Training as Adaptive Regularization Stefan Wager, Sida Wang, Percy S. Liang
New Subsampling Algorithms for Fast Least Squares Regression Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent Shai Shalev-Shwartz, Tong Zhang
Improved and Generalized Upper Bounds on the Complexity of Policy Iteration Bruno Scherrer
Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation Dahua Lin
Online Robust PCA via Stochastic Optimization Jiashi Feng, Huan Xu, Shuicheng Yan
Least Informative Dimensions Fabian Sinz, Anna Stockl, Jan Grewe, Jan Benda
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks Junming Yin, Qirong Ho, Eric P. Xing
Understanding variable importances in forests of randomized trees Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
Correlated random features for fast semi-supervised learning Brian McWilliams, David Balduzzi, Joachim M. Buhmann
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin
Better Approximation and Faster Algorithm Using the Proximal Average Yao-Liang Yu
Rapid Distance-Based Outlier Detection via Sampling Mahito Sugiyama, Karsten Borgwardt
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima Po-Ling Loh, Martin J. Wainwright
Non-Linear Domain Adaptation with Boosting Carlos J. Becker, Christos M. Christoudias, Pascal Fua
Mid-level Visual Element Discovery as Discriminative Mode Seeking Carl Doersch, Abhinav Gupta, Alexei A. Efros
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions Assaf Glazer, Michael Lindenbaum, Shaul Markovitch
Auditing: Active Learning with Outcome-Dependent Query Costs Sivan Sabato, Anand D. Sarwate, Nati Srebro
A message-passing algorithm for multi-agent trajectory planning Jose Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia
Learning Stochastic Feedforward Neural Networks Yichuan Tang, Ruslan R. Salakhutdinov
Inferring neural population dynamics from multiple partial recordings of the same neural circuit Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob Macke
Multi-Prediction Deep Boltzmann Machines Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio
Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation Vibhav Vineet, Carsten Rother, Philip Torr
Blind Calibration in Compressed Sensing using Message Passing Algorithms Christophe Schulke, Francesco Caltagirone, Florent Krzakala, Lenka Zdeborová
Learning Trajectory Preferences for Manipulators via Iterative Improvement Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena
Large Scale Distributed Sparse Precision Estimation Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
Neural representation of action sequences: how far can a simple snippet-matching model take us? Cheston Tan, Jedediah M. Singer, Thomas Serre, David Sheinberg, Tomaso Poggio
On Algorithms for Sparse Multi-factor NMF Siwei Lyu, Xin Wang
Dirty Statistical Models Eunho Yang, Pradeep K. Ravikumar
Parallel Sampling of DP Mixture Models using Sub-Cluster Splits Jason Chang, John W. Fisher III
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent Tianbao Yang
Prior-free and prior-dependent regret bounds for Thompson Sampling Sebastien Bubeck, Che-Yu Liu
Structured Learning via Logistic Regression Justin Domke
Which Space Partitioning Tree to Use for Search? Parikshit Ram, Alexander Gray
Projecting Ising Model Parameters for Fast Mixing Justin Domke, Xianghang Liu
Mixed Optimization for Smooth Functions Mehrdad Mahdavi, Lijun Zhang, Rong Jin
Conditional Random Fields via Univariate Exponential Families Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation Edo M. Airoldi, Thiago B. Costa, Stanley H. Chan
Reinforcement Learning in Robust Markov Decision Processes Shiau Hong Lim, Huan Xu, Shie Mannor
On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization Ke Hou, Zirui Zhou, Anthony Man-Cho So, Zhi-Quan Luo
Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems Hesham Mostafa, Lorenz. K. Mueller, Giacomo Indiveri
Latent Structured Active Learning Wenjie Luo, Alex Schwing, Raquel Urtasun
A Gang of Bandits Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella
Learning Feature Selection Dependencies in Multi-task Learning Daniel Hernández-Lobato, José Miguel Hernández-Lobato
B-test: A Non-parametric, Low Variance Kernel Two-sample Test Wojciech Zaremba, Arthur Gretton, Matthew Blaschko
Online PCA for Contaminated Data Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis Bach, Eric Moulines
Efficient Algorithm for Privately Releasing Smooth Queries Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang
Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search Anshumali Shrivastava, Ping Li
Unsupervised Spectral Learning of Finite State Transducers Raphael Bailly, Xavier Carreras, Ariadna Quattoni
Learning a Deep Compact Image Representation for Visual Tracking Naiyan Wang, Dit-Yan Yeung
Learning Multi-level Sparse Representations Ferran Diego Andilla, Fred A. Hamprecht
Robust Data-Driven Dynamic Programming Grani Adiwena Hanasusanto, Daniel Kuhn
Low-Rank Matrix and Tensor Completion via Adaptive Sampling Akshay Krishnamurthy, Aarti Singh
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms Adrien Todeschini, François Caron, Marie Chavent
Distributed Exploration in Multi-Armed Bandits Eshcar Hillel, Zohar S. Karnin, Tomer Koren, Ronny Lempel, Oren Somekh
The Pareto Regret Frontier Wouter M. Koolen
Direct 0-1 Loss Minimization and Margin Maximization with Boosting Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang
Regret based Robust Solutions for Uncertain Markov Decision Processes Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Speeding up Permutation Testing in Neuroimaging Chris Hinrichs, Vamsi K. Ithapu, Qinyuan Sun, Sterling C. Johnson, Vikas Singh
Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent
Supervised Sparse Analysis and Synthesis Operators Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M. Bronstein, Guillermo Sapiro
Low-rank matrix reconstruction and clustering via approximate message passing Ryosuke Matsushita, Toshiyuki Tanaka
Reasoning With Neural Tensor Networks for Knowledge Base Completion Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng
Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Ng
Estimating LASSO Risk and Noise Level Mohsen Bayati, Murat A. Erdogdu, Andrea Montanari
Learning Adaptive Value of Information for Structured Prediction David J. Weiss, Ben Taskar
Efficient Online Inference for Bayesian Nonparametric Relational Models Dae Il Kim, Prem K. Gopalan, David Blei, Erik Sudderth
Approximate inference in latent Gaussian-Markov models from continuous time observations Botond Cseke, Manfred Opper, Guido Sanguinetti
Linear Convergence with Condition Number Independent Access of Full Gradients Lijun Zhang, Mehrdad Mahdavi, Rong Jin
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements Divyanshu Vats, Richard Baraniuk
Wavelets on Graphs via Deep Learning Raif Rustamov, Leonidas J. Guibas
Robust Spatial Filtering with Beta Divergence Wojciech Samek, Duncan Blythe, Klaus-Robert Müller, Motoaki Kawanabe
Convex Relaxations for Permutation Problems Fajwel Fogel, Rodolphe Jenatton, Francis Bach, Alexandre D'Aspremont
High-Dimensional Gaussian Process Bandits Josip Djolonga, Andreas Krause, Volkan Cevher
A memory frontier for complex synapses Subhaneil Lahiri, Surya Ganguli
Marginals-to-Models Reducibility Tim Roughgarden, Michael Kearns
First-order Decomposition Trees Nima Taghipour, Jesse Davis, Hendrik Blockeel
A Comparative Framework for Preconditioned Lasso Algorithms Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan
Lasso Screening Rules via Dual Polytope Projection Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye
Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent Yuening Hu, Jordan L. Boyd-Graber, Hal Daume III, Z. Irene Ying
A Latent Source Model for Nonparametric Time Series Classification George H. Chen, Stanislav Nikolov, Devavrat Shah
Efficient Optimization for Sparse Gaussian Process Regression Yanshuai Cao, Marcus A. Brubaker, David J. Fleet, Aaron Hertzmann
Lexical and Hierarchical Topic Regression Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik
Stochastic Convex Optimization with Multiple Objectives Mehrdad Mahdavi, Tianbao Yang, Rong Jin
A Kernel Test for Three-Variable Interactions Dino Sejdinovic, Arthur Gretton, Wicher Bergsma
Memoized Online Variational Inference for Dirichlet Process Mixture Models Michael C. Hughes, Erik Sudderth
Designed Measurements for Vector Count Data Liming Wang, David E. Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank, Lawrence Carin
Robust Transfer Principal Component Analysis with Rank Constraints Yuhong Guo
Online Learning with Switching Costs and Other Adaptive Adversaries Nicolò Cesa-Bianchi, Ofer Dekel, Ohad Shamir
Learning Prices for Repeated Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
Probabilistic Principal Geodesic Analysis Miaomiao Zhang, P.T. Fletcher
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models Adel Javanmard, Andrea Montanari
Learning with Noisy Labels Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
Tracking Time-varying Graphical Structure Erich Kummerfeld, David Danks
Factorized Asymptotic Bayesian Inference for Latent Feature Models Kohei Hayashi, Ryohei Fujimaki
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, Eric P. Xing
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models Jie Liu, David Page
Online Learning with Costly Features and Labels Navid Zolghadr, Gabor Bartok, Russell Greiner, András György, Csaba Szepesvari
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions Eftychios A. Pnevmatikakis, Liam Paninski
A Novel Two-Step Method for Cross Language Representation Learning Min Xiao, Yuhong Guo
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations Tamir Hazan, Subhransu Maji, Tommi Jaakkola
Graphical Models for Inference with Missing Data Karthika Mohan, Judea Pearl, Jin Tian
Reshaping Visual Datasets for Domain Adaptation Boqing Gong, Kristen Grauman, Fei Sha
Statistical Active Learning Algorithms Maria-Florina F. Balcan, Vitaly Feldman
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski
Reflection methods for user-friendly submodular optimization Stefanie Jegelka, Francis Bach, Suvrit Sra
Unsupervised Structure Learning of Stochastic And-Or Grammars Kewei Tu, Maria Pavlovskaia, Song-Chun Zhu
Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka, Taiji Suzuki
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs Yann Dauphin, Yoshua Bengio
Learning Chordal Markov Networks by Constraint Satisfaction Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik Nyman, Johan Pensar
Parametric Task Learning Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima
A Deep Architecture for Matching Short Texts Zhengdong Lu, Hang Li
Computing the Stationary Distribution Locally Christina E. Lee, Asuman Ozdaglar, Devavrat Shah
Nonparametric Multi-group Membership Model for Dynamic Networks Myunghwan Kim, Jure Leskovec
Adaptive Step-Size for Policy Gradient Methods Matteo Pirotta, Marcello Restelli, Luca Bascetta
Optimistic Concurrency Control for Distributed Unsupervised Learning Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan
Reservoir Boosting : Between Online and Offline Ensemble Learning Leonidas Lefakis, François Fleuret
Multiclass Total Variation Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James von Brecht
Approximate Inference in Continuous Determinantal Processes Raja Hafiz Affandi, Emily Fox, Ben Taskar
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi
Thompson Sampling for 1-Dimensional Exponential Family Bandits Nathaniel Korda, Emilie Kaufmann, Remi Munos
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion Nguyen Viet Cuong, Wee Sun Lee, Nan Ye, Kian Ming A. Chai, Hai Leong Chieu
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari
Inverse Density as an Inverse Problem: the Fredholm Equation Approach Qichao Que, Mikhail Belkin
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising Forest Agostinelli, Michael R. Anderson, Honglak Lee
EDML for Learning Parameters in Directed and Undirected Graphical Models Khaled S. Refaat, Arthur Choi, Adnan Darwiche
Similarity Component Analysis Soravit Changpinyo, Kuan Liu, Fei Sha
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Josh Tenenbaum
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation John Duchi, Martin J. Wainwright, Michael I. Jordan
Firing rate predictions in optimal balanced networks David G. Barrett, Sophie Denève, Christian K. Machens
Manifold-based Similarity Adaptation for Label Propagation Masayuki Karasuyama, Hiroshi Mamitsuka
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty Haichao Zhang, David Wipf
Near-Optimal Entrywise Sampling for Data Matrices Dimitris Achlioptas, Zohar S. Karnin, Edo Liberty
Learning to Prune in Metric and Non-Metric Spaces Leonid Boytsov, Bilegsaikhan Naidan
Online learning in episodic Markovian decision processes by relative entropy policy search Alexander Zimin, Gergely Neu
Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result Paul Wagner
Bayesian Hierarchical Community Discovery Charles Blundell, Yee Whye Teh
From Bandits to Experts: A Tale of Domination and Independence Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour
Predictive PAC Learning and Process Decompositions Cosma Shalizi, Aryeh Kontorovich
Pass-efficient unsupervised feature selection Crystal Maung, Haim Schweitzer
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors Xiaoqin Zhang, Di Wang, Zhengyuan Zhou, Yi Ma
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search Aijun Bai, Feng Wu, Xiaoping Chen
Solving inverse problem of Markov chain with partial observations Tetsuro Morimura, Takayuki Osogami, Tsuyoshi Ide
Locally Adaptive Bayesian Multivariate Time Series Daniele Durante, Bruno Scarpa, David B. Dunson
Mapping paradigm ontologies to and from the brain Yannick Schwartz, Bertrand Thirion, Gael Varoquaux
Noise-Enhanced Associative Memories Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney
Exact and Stable Recovery of Pairwise Interaction Tensors Shouyuan Chen, Michael R. Lyu, Irwin King, Zenglin Xu
Bayesian entropy estimation for binary spike train data using parametric prior knowledge Evan W. Archer, Il Memming Park, Jonathan W. Pillow
Perfect Associative Learning with Spike-Timing-Dependent Plasticity Christian Albers, Maren Westkott, Klaus Pawelzik
On Poisson Graphical Models Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu
Streaming Variational Bayes Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
Gaussian Process Conditional Copulas with Applications to Financial Time Series José Miguel Hernández-Lobato, James R. Lloyd, Daniel Hernández-Lobato
Extracting regions of interest from biological images with convolutional sparse block coding Marius Pachitariu, Adam M. Packer, Noah Pettit, Henry Dalgleish, Michael Hausser, Maneesh Sahani
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer
Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections Matthew Lawlor, Steven W. Zucker
DESPOT: Online POMDP Planning with Regularization Adhiraj Somani, Nan Ye, David Hsu, Wee Sun Lee
Matrix Completion From any Given Set of Observations Troy Lee, Adi Shraibman
Regression-tree Tuning in a Streaming Setting Samory Kpotufe, Francesco Orabona
Multiscale Dictionary Learning for Estimating Conditional Distributions Francesca Petralia, Joshua T. Vogelstein, David B. Dunson
Dimension-Free Exponentiated Gradient Francesco Orabona
Stochastic Optimization of PCA with Capped MSG Raman Arora, Andy Cotter, Nati Srebro
On Flat versus Hierarchical Classification in Large-Scale Taxonomies Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini
Learning Gaussian Graphical Models with Observed or Latent FVSs Ying Liu, Alan Willsky
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies Yangqing Jia, Joshua T. Abbott, Joseph L. Austerweil, Thomas Griffiths, Trevor Darrell
Robust Bloom Filters for Large MultiLabel Classification Tasks Moustapha M. Cisse, Nicolas Usunier, Thierry Artières, Patrick Gallinari
Solving the multi-way matching problem by permutation synchronization Deepti Pachauri, Risi Kondor, Vikas Singh
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures Daniel Bartz, Klaus-Robert Müller
Top-Down Regularization of Deep Belief Networks Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi Jaakkola
Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms Yu Zhang
Machine Teaching for Bayesian Learners in the Exponential Family Xiaojin Zhu
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? Qiang Liu, Alexander T. Ihler, Mark Steyvers
Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths Stefan Mathe, Cristian Sminchisescu
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek, Richard Zemel, Ryan P. Adams
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model Fang Han, Han Liu
Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic James L. Sharpnack, Akshay Krishnamurthy, Aarti Singh
Demixing odors - fast inference in olfaction Agnieszka Grabska-Barwinska, Jeff Beck, Alexandre Pouget, Peter Latham
Learning Multiple Models via Regularized Weighting Daniel Vainsencher, Shie Mannor, Huan Xu
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
Distributed k-means and k-median Clustering on General Topologies Maria-Florina F. Balcan, Steven Ehrlich, Yingyu Liang
Multi-Task Bayesian Optimization Kevin Swersky, Jasper Snoek, Ryan P. Adams
Online Learning of Dynamic Parameters in Social Networks Shahin Shahrampour, Sasha Rakhlin, Ali Jadbabaie
A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles Jinwoo Shin, Andrew E. Gelfand, Misha Chertkov
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Xinhua Zhang, Wee Sun Lee, Yee Whye Teh
Approximate Gaussian process inference for the drift function in stochastic differential equations Andreas Ruttor, Philipp Batz, Manfred Opper
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause
Adaptive Market Making via Online Learning Jacob Abernethy, Satyen Kale
On the Sample Complexity of Subspace Learning Alessandro Rudi, Guillermo D. Canas, Lorenzo Rosasco
Spike train entropy-rate estimation using hierarchical Dirichlet process priors Karin C. Knudson, Jonathan W. Pillow
Embed and Project: Discrete Sampling with Universal Hashing Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
Discriminative Transfer Learning with Tree-based Priors Nitish Srivastava, Ruslan R. Salakhutdinov
Small-Variance Asymptotics for Hidden Markov Models Anirban Roychowdhury, Ke Jiang, Brian Kulis
Convergence of Monte Carlo Tree Search in Simultaneous Move Games Viliam Lisy, Vojta Kovarik, Marc Lanctot, Branislav Bosansky
DeViSE: A Deep Visual-Semantic Embedding Model Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, Tomas Mikolov
Reward Mapping for Transfer in Long-Lived Agents Xiaoxiao Guo, Satinder Singh, Richard L. Lewis
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation Martin Azizyan, Aarti Singh, Larry Wasserman
Predicting Parameters in Deep Learning Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas
Estimating the Unseen: Improved Estimators for Entropy and other Properties Paul Valiant, Gregory Valiant
What do row and column marginals reveal about your dataset? Behzad Golshan, John Byers, Evimaria Terzi
RNADE: The real-valued neural autoregressive density-estimator Benigno Uria, Iain Murray, Hugo Larochelle
Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards Thomas Bonald, Alexandre Proutiere
Reconciling "priors" & "priors" without prejudice? Remi Gribonval, Pierre Machart
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis Nikhil Rao, Christopher Cox, Rob Nowak, Timothy T. Rogers
Sensor Selection in High-Dimensional Gaussian Trees with Nuisances Daniel S. Levine, Jonathan P. How
Sequential Transfer in Multi-armed Bandit with Finite Set of Models Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill
Buy-in-Bulk Active Learning Liu Yang, Jaime Carbonell
Contrastive Learning Using Spectral Methods James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams
Message Passing Inference with Chemical Reaction Networks Nils E. Napp, Ryan P. Adams
Eluder Dimension and the Sample Complexity of Optimistic Exploration Dan Russo, Benjamin Van Roy
Learning word embeddings efficiently with noise-contrastive estimation Andriy Mnih, Koray Kavukcuoglu
Sparse Inverse Covariance Estimation with Calibration Tuo Zhao, Han Liu
Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization Julien Mairal
Sinkhorn Distances: Lightspeed Computation of Optimal Transport Marco Cuturi
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning Miao Xu, Rong Jin, Zhi-Hua Zhou
Compete to Compute Rupesh K. Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Juergen Schmidhuber
Fast Determinantal Point Process Sampling with Application to Clustering Byungkon Kang
Information-theoretic lower bounds for distributed statistical estimation with communication constraints Yuchen Zhang, John Duchi, Michael I. Jordan, Martin J. Wainwright
Projected Natural Actor-Critic Philip S. Thomas, William C. Dabney, Stephen Giguere, Sridhar Mahadevan
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal Jacob Abernethy, Peter L. Bartlett, Rafael Frongillo, Andre Wibisono
Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests Yacine Jernite, Yonatan Halpern, David Sontag
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion Franz Kiraly, Louis Theran
Learning the Local Statistics of Optical Flow Dan Rosenbaum, Daniel Zoran, Yair Weiss
Aggregating Optimistic Planning Trees for Solving Markov Decision Processes Gunnar Kedenburg, Raphael Fonteneau, Remi Munos
Robust learning of low-dimensional dynamics from large neural ensembles David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski
Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising Min Xu, Tao Qin, Tie-Yan Liu
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization Nataliya Shapovalova, Michalis Raptis, Leonid Sigal, Greg Mori
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables Jing Xiang, Seyoung Kim
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh K. Iyer, Jeff A. Bilmes
Scalable Inference for Logistic-Normal Topic Models Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang
Spectral methods for neural characterization using generalized quadratic models Il Memming Park, Evan W. Archer, Nicholas Priebe, Jonathan W. Pillow
Universal models for binary spike patterns using centered Dirichlet processes Il Memming Park, Evan W. Archer, Kenneth Latimer, Jonathan W. Pillow
Synthesizing Robust Plans under Incomplete Domain Models Tuan A. Nguyen, Subbarao Kambhampati, Minh Do
Integrated Non-Factorized Variational Inference Shaobo Han, Xuejun Liao, Lawrence Carin
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions Ari Pakman, Liam Paninski
Symbolic Opportunistic Policy Iteration for Factored-Action MDPs Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions Yasin Abbasi, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvari
Flexible sampling of discrete data correlations without the marginal distributions Alfredo Kalaitzis, Ricardo Silva
One-shot learning by inverting a compositional causal process Brenden M. Lake, Ruslan R. Salakhutdinov, Josh Tenenbaum
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. Michel Besserve, Nikos K. Logothetis, Prof. Bernhard Schölkopf
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis James R. Voss, Luis Rademacher, Mikhail Belkin
Deep Neural Networks for Object Detection Christian Szegedy, Alexander Toshev, Dumitru Erhan
Geometric optimisation on positive definite matrices for elliptically contoured distributions Suvrit Sra, Reshad Hosseini
Sign Cauchy Projections and Chi-Square Kernel Ping Li, Gennady Samorodnitsk, John Hopcroft
Relevance Topic Model for Unstructured Social Group Activity Recognition Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan
k-Prototype Learning for 3D Rigid Structures Hu Ding, Ronald Berezney, Jinhui Xu
Restricting exchangeable nonparametric distributions Sinead A. Williamson, Steve N. MacEachern, Eric P. Xing
Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting Shunan Zhang, Angela J. Yu
Probabilistic Movement Primitives Alexandros Paraschos, Christian Daniel, Jan R. Peters, Gerhard Neumann
Policy Shaping: Integrating Human Feedback with Reinforcement Learning Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles Isbell, Andrea L. Thomaz
Multilinear Dynamical Systems for Tensor Time Series Mark Rogers, Lei Li, Stuart J. Russell
Deep content-based music recommendation Aaron van den Oord, Sander Dieleman, Benjamin Schrauwen
A Stability-based Validation Procedure for Differentially Private Machine Learning Kamalika Chaudhuri, Staal A. Vinterbo
Capacity of strong attractor patterns to model behavioural and cognitive prototypes Abbas Edalat
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA Vincent Q. Vu, Juhee Cho, Jing Lei, Karl Rohe
Cluster Trees on Manifolds Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman
Bayesian inference for low rank spatiotemporal neural receptive fields Mijung Park, Jonathan W. Pillow
Adaptive Submodular Maximization in Bandit Setting Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan
Generalized Method-of-Moments for Rank Aggregation Hossein Azari Soufiani, William Chen, David C. Parkes, Lirong Xia
Analyzing Hogwild Parallel Gaussian Gibbs Sampling Matthew Johnson, James Saunderson, Alan Willsky
Minimax Optimal Algorithms for Unconstrained Linear Optimization Brendan McMahan, Jacob Abernethy
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings Abhradeep Guha Thakurta, Adam Smith
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes
Σ-Optimality for Active Learning on Gaussian Random Fields Yifei Ma, Roman Garnett, Jeff Schneider
Learning Kernels Using Local Rademacher Complexity Corinna Cortes, Marius Kloft, Mehryar Mohri
Annealing between distributions by averaging moments Roger B. Grosse, Chris J. Maddison, Ruslan R. Salakhutdinov
Optimizing Instructional Policies Robert V. Lindsey, Michael C. Mozer, William J. Huggins, Harold Pashler
Translating Embeddings for Modeling Multi-relational Data Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
Phase Retrieval using Alternating Minimization Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
Real-Time Inference for a Gamma Process Model of Neural Spiking David E. Carlson, Vinayak Rao, Joshua T. Vogelstein, Lawrence Carin
Understanding Dropout Pierre Baldi, Peter J. Sadowski
The Power of Asymmetry in Binary Hashing Behnam Neyshabur, Nati Srebro, Ruslan R. Salakhutdinov, Yury Makarychev, Payman Yadollahpour
Estimation, Optimization, and Parallelism when Data is Sparse John Duchi, Michael I. Jordan, Brendan McMahan
A multi-agent control framework for co-adaptation in brain-computer interfaces Josh S. Merel, Roy Fox, Tony Jebara, Liam Paninski
Modeling Overlapping Communities with Node Popularities Prem K. Gopalan, Chong Wang, David Blei
Learning from Limited Demonstrations Beomjoon Kim, Amir massoud Farahmand, Joelle Pineau, Doina Precup
On the Complexity and Approximation of Binary Evidence in Lifted Inference Guy Van den Broeck, Adnan Darwiche
On the Representational Efficiency of Restricted Boltzmann Machines James Martens, Arkadev Chattopadhya, Toni Pitassi, Richard Zemel
Memory Limited, Streaming PCA Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
An Approximate, Efficient LP Solver for LP Rounding Srikrishna Sridhar, Stephen Wright, Christopher Re, Ji Liu, Victor Bittorf, Ce Zhang
Linear decision rule as aspiration for simple decision heuristics Özgür Şimşek
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation Harikrishna Narasimhan, Shivani Agarwal
Bayesian inference as iterated random functions with applications to sequential inference in graphical models Arash Amini, XuanLong Nguyen
Compressive Feature Learning Hristo S. Paskov, Robert West, John C. Mitchell, Trevor Hastie
Moment-based Uniform Deviation Bounds for k-means and Friends Matus J. Telgarsky, Sanjoy Dasgupta
Fast Template Evaluation with Vector Quantization Mohammad Amin Sadeghi, David Forsyth
Context-sensitive active sensing in humans Sheeraz Ahmad, He Huang, Angela J. Yu
A New Convex Relaxation for Tensor Completion Bernardino Romera-Paredes, Massimiliano Pontil
Variational Planning for Graph-based MDPs Qiang Cheng, Qiang Liu, Feng Chen, Alexander T. Ihler
Convex Two-Layer Modeling Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans
Sketching Structured Matrices for Faster Nonlinear Regression Haim Avron, Vikas Sindhwani, David Woodruff
(More) Efficient Reinforcement Learning via Posterior Sampling Ian Osband, Dan Russo, Benjamin Van Roy
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition Adel Javanmard, Andrea Montanari
Efficient Exploration and Value Function Generalization in Deterministic Systems Zheng Wen, Benjamin Van Roy
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Amir massoud Farahmand, Joelle Pineau, Doina Precup
Learning and using language via recursive pragmatic reasoning about other agents Nathaniel J. Smith, Noah Goodman, Michael Frank
Learning Stochastic Inverses Andreas Stuhlmüller, Jacob Taylor, Noah Goodman
Learning invariant representations and applications to face verification Qianli Liao, Joel Z. Leibo, Tomaso Poggio
Optimization, Learning, and Games with Predictable Sequences Sasha Rakhlin, Karthik Sridharan
Adaptivity to Local Smoothness and Dimension in Kernel Regression Samory Kpotufe, Vikas Garg
Adaptive dropout for training deep neural networks Jimmy Ba, Brendan Frey
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral StreamDaniel L. Yamins, Ha Hong, Charles Cadieu, James J. DiCarlo
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex Sam Patterson, Yee Whye Teh
Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, Jeff Dean
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel Tai Qin, Karl Rohe
Analyzing the Harmonic Structure in Graph-Based Learning Xiao-Ming Wu, Zhenguo Li, Shih-Fu Chang
Recurrent linear models of simultaneously-recorded neural populations Marius Pachitariu, Biljana Petreska, Maneesh Sahani
Scalable Influence Estimation in Continuous-Time Diffusion Networks Nan Du, Le Song, Manuel Gomez Rodriguez, Hongyuan Zha
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl Edward Rasmussen
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep K. Ravikumar, Russell Poldrack
The Fast Convergence of Incremental PCA Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund
Multisensory Encoding, Decoding, and Identification Aurel A. Lazar, Yevgeniy Slutskiy
Adaptive Anonymity via b-Matching Krzysztof M. Choromanski, Tony Jebara, Kui Tang
Optimal integration of visual speed across different spatiotemporal frequency channels Matjaz Jogan, Alan A. Stocker
Matrix factorization with binary components Martin Slawski, Matthias Hein, Pavlo Lutsik
Learning to Pass Expectation Propagation Messages Nicolas Heess, Daniel Tarlow, John Winn
Robust Low Rank Kernel Embeddings of Multivariate Distributions Le Song, Bo Dai

Advances in Neural Information Processing Systems 27 (NIPS 2014)

The papers below appear in Advances in Neural Information Processing Systems 27 edited by Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger.
They are proceedings from the conference, "Neural Information Processing Systems 2014."

Kernel Mean Estimation via Spectral Filtering Krikamol Muandet, Bharath Sriperumbudur, Prof. Bernhard Schölkopf
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Yichuan Zhang, Charles Sutton
Communication Efficient Distributed Machine Learning with the Parameter Server Mu Li, David G. Andersen, Alexander J. Smola, Kai Yu
The Infinite Mixture of Infinite Gaussian Mixtures Halid Z. Yerebakan, Bartek Rajwa, Murat Dundar
Robust Classification Under Sample Selection Bias Anqi Liu, Brian Ziebart
Zeta Hull Pursuits: Learning Nonconvex Data Hulls Yuanjun Xiong, Wei Liu, Deli Zhao, Xiaoou Tang
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction Katerina Fragkiadaki, Marta Salas, Pablo Arbelaez, Jitendra Malik
Sparse Space-Time Deconvolution for Calcium Image Analysis Ferran Diego Andilla, Fred A. Hamprecht
Restricted Boltzmann machines modeling human choice Takayuki Osogami, Makoto Otsuka
Multiscale Fields of Patterns Pedro Felzenszwalb, John G. Oberlin
large scale canonical correlation analysis with iterative least squares Yichao Lu, Dean P. Foster
Altitude Training: Strong Bounds for Single-Layer Dropout Stefan Wager, William Fithian, Sida Wang, Percy S. Liang
Rounding-based Moves for Metric Labeling M. Pawan Kumar
Parallel Double Greedy Submodular Maximization Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan
Multivariate Regression with Calibration Han Liu, Lie Wang, Tuo Zhao
Exact Post Model Selection Inference for Marginal Screening Jason D. Lee, Jonathan E. Taylor
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification Yingzhen Yang, Feng Liang, Shuicheng Yan, Zhangyang Wang, Thomas S. Huang
Just-In-Time Learning for Fast and Flexible Inference S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation Ohad Shamir
Quantized Kernel Learning for Feature Matching Danfeng Qin, Xuanli Chen, Matthieu Guillaumin, Luc V. Gool
Parallel Direction Method of Multipliers Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
(Almost) No Label No Cry Giorgio Patrini, Richard Nock, Paul Rivera, Tiberio Caetano
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards Omar Besbes, Yonatan Gur, Assaf Zeevi
Object Localization based on Structural SVM using Privileged Information Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
Shape and Illumination from Shading using the Generic Viewpoint Assumption Daniel Zoran, Dilip Krishnan, Jose Bento, Bill Freeman
Parallel Sampling of HDPs using Sub-Cluster Splits Jason Chang, John W. Fisher III
From MAP to Marginals: Variational Inference in Bayesian Submodular Models Josip Djolonga, Andreas Krause
Robust Logistic Regression and Classification Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities Tianbao Yang, Rong Jin
A Unified Semantic Embedding: Relating Taxonomies and Attributes Sung Ju Hwang, Leonid Sigal
Transportability from Multiple Environments with Limited Experiments: Completeness Results Elias Bareinboim, Judea Pearl
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Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation Jonathan J. Tompson, Arjun Jain, Yann LeCun, Christoph Bregler
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Deep Fragment Embeddings for Bidirectional Image Sentence Mapping Andrej Karpathy, Armand Joulin, Fei Fei F. Li
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Modeling Deep Temporal Dependencies with Recurrent Grammar Cells"" Vincent Michalski, Roland Memisevic, Kishore Konda
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Structure Regularization for Structured Prediction Xu Sun
On Multiplicative Multitask Feature Learning Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun
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Controlling privacy in recommender systems Yu Xin, Tommi Jaakkola
Convolutional Kernel Networks Julien Mairal, Piotr Koniusz, Zaid Harchaoui, Cordelia Schmid
Fairness in Multi-Agent Sequential Decision-Making Chongjie Zhang, Julie A. Shah
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets Adarsh Prasad, Stefanie Jegelka, Dhruv Batra
Do Deep Nets Really Need to be Deep? Jimmy Ba, Rich Caruana
Dynamic Rank Factor Model for Text Streams Shaobo Han, Lin Du, Esther Salazar, Lawrence Carin
Generative Adversarial Nets Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
Testing Unfaithful Gaussian Graphical Models De Wen Soh, Sekhar C. Tatikonda
Global Sensitivity Analysis for MAP Inference in Graphical Models Jasper De Bock, Cassio P. de Campos, Alessandro Antonucci
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain Charles Y. Zheng, Franco Pestilli, Ariel Rokem
Efficient Minimax Signal Detection on Graphs Jing Qian, Venkatesh Saligrama
Cone-Constrained Principal Component Analysis Yash Deshpande, Andrea Montanari, Emile Richard
On Communication Cost of Distributed Statistical Estimation and Dimensionality Ankit Garg, Tengyu Ma, Huy Nguyen
Computing Nash Equilibria in Generalized Interdependent Security Games Hau Chan, Luis E. Ortiz
Consistent Binary Classification with Generalized Performance Metrics Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon
Greedy Subspace Clustering Dohyung Park, Constantine Caramanis, Sujay Sanghavi
Deterministic Symmetric Positive Semidefinite Matrix Completion William E. Bishop, Byron M. Yu
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere
Online combinatorial optimization with stochastic decision sets and adversarial losses Gergely Neu, Michal Valko
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature Tom Gunter, Michael A. Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts
Multi-Scale Spectral Decomposition of Massive Graphs Si Si, Donghyuk Shin, Inderjit S. Dhillon, Beresford N. Parlett
The limits of squared Euclidean distance regularization Michal Derezinski, Manfred K. Warmuth
Bregman Alternating Direction Method of Multipliers Huahua Wang, Arindam Banerjee
Multitask learning meets tensor factorization: task imputation via convex optimization Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing
Scalable Inference for Neuronal Connectivity from Calcium Imaging Alyson K. Fletcher, Sundeep Rangan
Structure learning of antiferromagnetic Ising models Guy Bresler, David Gamarnik, Devavrat Shah
The Noisy Power Method: A Meta Algorithm with Applications Moritz Hardt, Eric Price
Algorithm selection by rational metareasoning as a model of human strategy selection Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Thomas Griffiths
Extremal Mechanisms for Local Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath
Global Belief Recursive Neural Networks Romain Paulus, Richard Socher, Christopher D. Manning
A statistical model for tensor PCA Emile Richard, Andrea Montanari
Real-Time Decoding of an Integrate and Fire Encoder Shreya Saxena, Munther Dahleh
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning Brendan McMahan, Matthew Streeter
On the Number of Linear Regions of Deep Neural Networks Guido F. Montufar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio
Extracting Latent Structure From Multiple Interacting Neural Populations Joao Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu
Learning with Fredholm Kernels Qichao Que, Mikhail Belkin, Yusu Wang
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models Michalis Titsias RC AUEB, Christopher Yau
Optimizing Energy Production Using Policy Search and Predictive State Representations Yuri Grinberg, Doina Precup, Michel Gendreau
Scaling-up Importance Sampling for Markov Logic Networks Deepak Venugopal, Vibhav G. Gogate
Optimal Neural Codes for Control and Estimation Alex K. Susemihl, Ron Meir, Manfred Opper
Graph Clustering With Missing Data: Convex Algorithms and Analysis Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
Scale Adaptive Blind Deblurring Haichao Zhang, Jianchao Yang
Weighted importance sampling for off-policy learning with linear function approximation A. Rupam Mahmood, Hado P. van Hasselt, Richard S. Sutton
Information-based learning by agents in unbounded state spaces Shariq A. Mobin, James A. Arnemann, Fritz Sommer
Exponential Concentration of a Density Functional Estimator Shashank Singh, Barnabas Poczos
Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina F. Balcan, Le Song
Fast Training of Pose Detectors in the Fourier Domain João F. Henriques, Pedro Martins, Rui F. Caseiro, Jorge Batista
An Accelerated Proximal Coordinate Gradient Method Qihang Lin, Zhaosong Lu, Lin Xiao
Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi, Virginia Smith, Martin Takac, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
Simple MAP Inference via Low-Rank Relaxations Roy Frostig, Sida Wang, Percy S. Liang, Christopher D. Manning
A* Sampling Chris J. Maddison, Daniel Tarlow, Tom Minka
A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc V. Le
Improved Distributed Principal Component Analysis Yingyu Liang, Maria-Florina F. Balcan, Vandana Kanchanapally, David Woodruff
Sparse Polynomial Learning and Graph Sketching Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam Klivans
Tight Continuous Relaxation of the Balanced k-Cut Problem Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Expectation-Maximization for Learning Determinantal Point Processes Jennifer A. Gillenwater, Alex Kulesza, Emily Fox, Ben Taskar
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs David I. Inouye, Pradeep K. Ravikumar, Inderjit S. Dhillon
Streaming, Memory Limited Algorithms for Community Detection Se-Young Yun, marc lelarge, Alexandre Proutiere
Content-based recommendations with Poisson factorization Prem K. Gopalan, Laurent Charlin, David Blei
A Statistical Decision-Theoretic Framework for Social Choice Hossein Azari Soufiani, David C. Parkes, Lirong Xia
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin
Bayesian Sampling Using Stochastic Gradient Thermostats Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven
On Sparse Gaussian Chain Graph Models Calvin McCarter, Seyoung Kim
Orbit Regularization Renato Negrinho, Andre Martins
Efficient Minimax Strategies for Square Loss Games Wouter M. Koolen, Alan Malek, Peter L. Bartlett
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs Miles Lopes
Large-Margin Convex Polytope Machine Alex Kantchelian, Michael C. Tschantz, Ling Huang, Peter L. Bartlett, Anthony D. Joseph, J. D. Tygar
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Yarin Gal, Mark van der Wilk, Carl Edward Rasmussen
Learning Distributed Representations for Structured Output Prediction Vivek Srikumar, Christopher D. Manning
Convex Deep Learning via Normalized Kernels Özlem Aslan, Xinhua Zhang, Dale Schuurmans
Tight convex relaxations for sparse matrix factorization Emile Richard, Guillaume R. Obozinski, Jean-Philippe Vert
Learning to Search in Branch and Bound Algorithms He He, Hal Daume III, Jason M. Eisner
An Integer Polynomial Programming Based Framework for Lifted MAP Inference Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav G. Gogate
Conditional Random Field Autoencoders for Unsupervised Structured Prediction Waleed Ammar, Chris Dyer, Noah A. Smith
How transferable are features in deep neural networks? Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson
Accelerated Mini-batch Randomized Block Coordinate Descent Method Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
A Latent Source Model for Online Collaborative Filtering Guy Bresler, George H. Chen, Devavrat Shah
Distributed Bayesian Posterior Sampling via Moment Sharing Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang
Learning with Pseudo-Ensembles Philip Bachman, Ouais Alsharif, Doina Precup
Learning Time-Varying Coverage Functions Nan Du, Yingyu Liang, Maria-Florina F. Balcan, Le Song
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time Zhaoran Wang, Huanran Lu, Han Liu
Discriminative Metric Learning by Neighborhood Gerrymandering Shubhendu Trivedi, David Mcallester, Greg Shakhnarovich
Finding a sparse vector in a subspace: Linear sparsity using alternating directions Qing Qu, Ju Sun, John Wright
Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh
Discrete Graph Hashing Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang
Feedback Detection for Live Predictors Stefan Wager, Nick Chamandy, Omkar Muralidharan, Amir Najmi
Rates of Convergence for Nearest Neighbor Classification Kamalika Chaudhuri, Sanjoy Dasgupta
Consistency of weighted majority votes Daniel Berend, Aryeh Kontorovich
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling Mingyuan Zhou
Zero-shot recognition with unreliable attributes Dinesh Jayaraman, Kristen Grauman
Concavity of reweighted Kikuchi approximation Po-Ling Loh, Andre Wibisono
Online Decision-Making in General Combinatorial Spaces Arun Rajkumar, Shivani Agarwal
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning Mohammad Taha Bahadori, Qi (Rose) Yu, Yan Liu
Clustered factor analysis of multineuronal spike data Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
Algorithms for CVaR Optimization in MDPs Yinlam Chow, Mohammad Ghavamzadeh
Factoring Variations in Natural Images with Deep Gaussian Mixture Models Aaron van den Oord, Benjamin Schrauwen
Partition-wise Linear Models Hidekazu Oiwa, Ryohei Fujimaki
LSDA: Large Scale Detection through Adaptation Judy Hoffman, Sergio Guadarrama, Eric S. Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko
Deep Networks with Internal Selective Attention through Feedback Connections Marijn F. Stollenga, Jonathan Masci, Faustino Gomez, Juergen Schmidhuber
Parallel Feature Selection Inspired by Group Testing Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q. Ngo, XuanLong Nguyen, Christopher Ré, Venu Govindaraju
Low-Rank Time-Frequency Synthesis Cédric Févotte, Matthieu Kowalski
Pre-training of Recurrent Neural Networks via Linear Autoencoders Luca Pasa, Alessandro Sperduti
Semi-supervised Learning with Deep Generative Models Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation Mingjun Zhong, Nigel Goddard, Charles Sutton
Stochastic variational inference for hidden Markov models Nicholas Foti, Jason Xu, Dillon Laird, Emily Fox
A Wild Bootstrap for Degenerate Kernel Tests Kacper P. Chwialkowski, Dino Sejdinovic, Arthur Gretton
Biclustering Using Message Passing Luke O'Connor, Soheil Feizi
Fast Kernel Learning for Multidimensional Pattern Extrapolation Andrew Wilson, Elad Gilboa, John P. Cunningham, Arye Nehorai
Learning on graphs using Orthonormal Representation is Statistically Consistent Rakesh Shivanna, Chiranjib Bhattacharyya
Spectral k-Support Norm Regularization Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
Unsupervised learning of an efficient short-term memory network Pietro Vertechi, Wieland Brendel, Christian K. Machens
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls Yuancheng Zhu, John Lafferty
Learning a Concept Hierarchy from Multi-labeled Documents Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik, Jonathan Chang
Variational Gaussian Process State-Space Models Roger Frigola, Yutian Chen, Carl Edward Rasmussen
Fast Prediction for Large-Scale Kernel Machines Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon

Advances in Neural Information Processing Systems 28 (NIPS 2015)

The papers below appear in Advances in Neural Information Processing Systems 28 edited by C. Cortes and N.D. Lawrence and D.D. Lee and M. Sugiyama and R. Garnett.
They are proceedings from the conference, "Neural Information Processing Systems 2015."

Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar Bhadresh Shah, Denny Zhou
Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen, Aditya Menon, Robert C. Williamson
Algorithmic Stability and Uniform Generalization Ibrahim M. Alabdulmohsin
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models Theodoros Tsiligkaridis, Theodoros Tsiligkaridis, Keith Forsythe
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey
Robust Portfolio Optimization Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Logarithmic Time Online Multiclass prediction Anna E. Choromanska, John Langford
Planar Ultrametrics for Image Segmentation Julian E. Yarkony, Charless Fowlkes
Expressing an Image Stream with a Sequence of Natural Sentences Cesc C. Park, Gunhee Kim
Parallel Correlation Clustering on Big Graphs Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
Space-Time Local Embeddings Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear MeasurementsQinqing Zheng, John Lafferty
Smooth Interactive Submodular Set Cover Bryan D. He, Yisong Yue
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Josh Tenenbaum
On the Pseudo-Dimension of Nearly Optimal Auctions Jamie H. Morgenstern, Tim Roughgarden
Unlocking neural population non-stationarities using hierarchical dynamics models Mijung Park, Gergo Bohner, Jakob H. Macke
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani
Color Constancy by Learning to Predict Chromaticity from Luminance Ayan Chakrabarti
Fast and Accurate Inference of Plackett–Luce Models Lucas Maystre, Matthias Grossglauser
Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Armand Joulin, Tomas Mikolov
Where are they looking? Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba
The Pareto Regret Frontier for Bandits Tor Lattimore
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors Andrea Montanari, Daniel Reichman, Ofer Zeitouni
Measuring Sample Quality with Stein's Method Jackson Gorham, Lester Mackey
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution Yan Huang, Wei Wang, Liang Wang
Bounding errors of Expectation-Propagation Guillaume P. Dehaene, Simon Barthelmé
A fast, universal algorithm to learn parametric nonlinear embeddings Miguel A. Carreira-Perpinan, Max Vladymyrov
Texture Synthesis Using Convolutional Neural Networks Leon Gatys, Alexander S. Ecker, Matthias Bethge
Extending Gossip Algorithms to Distributed Estimation of U-statistics Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon
Streaming, Distributed Variational Inference for Bayesian Nonparametrics Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
Learning visual biases from human imagination Carl Vondrick, Hamed Pirsiavash, Aude Oliva, Antonio Torralba
Smooth and Strong: MAP Inference with Linear Convergence Ofer Meshi, Mehrdad Mahdavi, Alex Schwing
Copeland Dueling Bandits Masrour Zoghi, Zohar S. Karnin, Shimon Whiteson, Maarten de Rijke
Optimal Ridge Detection using Coverage Risk Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman
Top-k Multiclass SVM Maksim Lapin, Matthias Hein, Bernt Schiele
Policy Evaluation Using the Ω-Return Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris
Orthogonal NMF through Subspace Exploration Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis
Stochastic Online Greedy Learning with Semi-bandit Feedbacks Tian Lin, Jian Li, Wei Chen
Deeply Learning the Messages in Message Passing Inference Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass
Accelerated Proximal Gradient Methods for Nonconvex Programming Huan Li, Zhouchen Lin
Approximating Sparse PCA from Incomplete Data ABHISEK KUNDU, Petros Drineas, Malik Magdon-Ismail
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, james m. robins
Column Selection via Adaptive Sampling Saurabh Paul, Malik Magdon-Ismail, Petros Drineas
HONOR: Hybrid Optimization for NOn-convex Regularized problems Pinghua Gong, Jieping Ye
3D Object Proposals for Accurate Object Class Detection Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits Huasen Wu, R. Srikant, Xin Liu, Chong Jiang
Tensorizing Neural Networks Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P. Vetrov
Parallelizing MCMC with Random Partition Trees Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
A Reduced-Dimension fMRI Shared Response Model Po-Hsuan (Cameron) Chen, Janice Chen, Yaara Yeshurun, Uri Hasson, James Haxby, Peter J. Ramadge
Spectral Learning of Large Structured HMMs for Comparative Epigenomics Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin Chen
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability Xia Qu, Prashant Doshi
Estimating Mixture Models via Mixtures of Polynomials Sida Wang, Arun Tejasvi Chaganty, Percy S. Liang
On the Global Linear Convergence of Frank-Wolfe Optimization Variants Simon Lacoste-Julien, Martin Jaggi
Deep Knowledge Tracing Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein
Rethinking LDA: Moment Matching for Discrete ICA Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors Sohail Bahmani, Justin Romberg
Barrier Frank-Wolfe for Marginal Inference Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag
Learning Theory and Algorithms for Forecasting Non-stationary Time Series Vitaly Kuznetsov, Mehryar Mohri
Compressive spectral embedding: sidestepping the SVD Dinesh Ramasamy, Upamanyu Madhow
A Nonconvex Optimization Framework for Low Rank Matrix Estimation Tuo Zhao, Zhaoran Wang, Han Liu
Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei
Attention-Based Models for Speech Recognition Jan K. Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, Yoshua Bengio
Closed-form Estimators for High-dimensional Generalized Linear Models Eunho Yang, Aurelie C. Lozano, Pradeep K. Ravikumar
Online F-Measure Optimization Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier
M-Best-Diverse Labelings for Submodular Energies and Beyond Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number Janne H. Korhonen, Pekka Parviainen
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring Gunwoong Park, Garvesh Raskutti
Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy Marylou Gabrie, Eric W. Tramel, Florent Krzakala
Character-level Convolutional Networks for Text Classification Xiang Zhang, Junbo Zhao, Yann LeCun
Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen
Black-box optimization of noisy functions with unknown smoothness Jean-Bastien Grill, Michal Valko, Remi Munos, Remi Munos
Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters Emmanuel Abbe, Colin Sandon
Deep learning with Elastic Averaging SGD Sixin Zhang, Anna E. Choromanska, Yann LeCun
Monotone k-Submodular Function Maximization with Size Constraints Naoto Ohsaka, Yuichi Yoshida
Active Learning from Weak and Strong Labelers Chicheng Zhang, Kamalika Chaudhuri
On the Optimality of Classifier Chain for Multi-label Classification Weiwei Liu, Ivor Tsang
Robust Regression via Hard Thresholding Kush Bhatia, Prateek Jain, Purushottam Kar
Sparse Local Embeddings for Extreme Multi-label Classification Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems Yuxin Chen, Emmanuel Candes
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure Peter Schulam, Suchi Saria
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector Chao Qu, Huan Xu
Sparse PCA via Bipartite Matchings Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis
Fast Randomized Kernel Ridge Regression with Statistical Guarantees Ahmed Alaoui, Michael W. Mahoney
Online Learning for Adversaries with Memory: Price of Past Mistakes Oren Anava, Elad Hazan, Shie Mannor
Convolutional spike-triggered covariance analysis for neural subunit models Anqi Wu, Il Memming Park, Jonathan W. Pillow
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian SHI, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun WOO
GAP Safe screening rules for sparse multi-task and multi-class models Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces Takashi Takenouchi, Takafumi Kanamori
Statistical Model Criticism using Kernel Two Sample Tests James R. Lloyd, Zoubin Ghahramani
Precision-Recall-Gain Curves: PR Analysis Done Right Peter Flach, Meelis Kull
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice Tasuku Soma, Yuichi Yoshida
Bidirectional Recurrent Neural Networks as Generative Models Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, Juha T. Karhunen
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu, Peter Richtarik, Tong Zhang
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets Justin Domke
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks Minhyung Cho, Chandra Dhir, Jaehyung Lee
Large-scale probabilistic predictors with and without guarantees of validity Vladimir Vovk, Ivan Petej, Valentina Fedorova
Shepard Convolutional Neural Networks Jimmy SJ Ren, Li Xu, Qiong Yan, Wenxiu Sun
Matrix Manifold Optimization for Gaussian Mixtures Reshad Hosseini, Suvrit Sra
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding Rie Johnson, Tong Zhang
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models Akihiro Kishimoto, Radu Marinescu, Adi Botea
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling Ming Liang, Xiaolin Hu, Bo Zhang
Bounding the Cost of Search-Based Lifted Inference David B. Smith, Vibhav G. Gogate
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton
Linear Multi-Resource Allocation with Semi-Bandit Feedback Tor Lattimore, Koby Crammer, Csaba Szepesvari
Unsupervised Learning by Program Synthesis Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
Enforcing balance allows local supervised learning in spiking recurrent networks Ralph Bourdoukan, Sophie Denève
Fast and Guaranteed Tensor Decomposition via Sketching Yining Wang, Hsiao-Yu Tung, Alexander J. Smola, Anima Anandkumar
Differentially private subspace clustering Yining Wang, Yu-Xiang Wang, Aarti Singh
Predtron: A Family of Online Algorithms for General Prediction Problems Prateek Jain, Nagarajan Natarajan, Ambuj Tewari
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization Fredrik D. Johansson, Ankani Chattoraj, Chiranjib Bhattacharyya, Devdatt Dubhashi
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk Guillaume Papa, Stéphan Clémençon, Aurélien Bellet
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs Wei Cao, Jian Li, Yufei Tao, Zhize Li
The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions Sebastian Bitzer, Stefan Kiebel
Fast Classification Rates for High-dimensional Gaussian Generative Models Tianyang Li, Adarsh Prasad, Pradeep K. Ravikumar
Fast Distributed k-Center Clustering with Outliers on Massive Data Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
Human Memory Search as Initial-Visit Emitting Random Walk Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers, Zhuoran Yang, ming yuan
Non-convex Statistical Optimization for Sparse Tensor Graphical Model Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng
Convergence Rates of Active Learning for Maximum Likelihood Estimation Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis Jimei Yang, Scott E. Reed, Ming-Hsuan Yang, Honglak Lee
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier
Backpropagation for Energy-Efficient Neuromorphic Computing Steve K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, Dharmendra S. Modha
Alternating Minimization for Regression Problems with Vector-valued Outputs Prateek Jain, Ambuj Tewari
Learning both Weights and Connections for Efficient Neural Network Song Han, Jeff Pool, John Tran, William Dally
Optimal Rates for Random Fourier Features Bharath Sriperumbudur, Zoltan Szabo
The Population Posterior and Bayesian Modeling on Streams James McInerney, Rajesh Ranganath, David Blei
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees François-Xavier Briol, Chris Oates, Mark Girolami, Michael A. Osborne
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer
Unified View of Matrix Completion under General Structural Constraints Suriya Gunasekar, Arindam Banerjee, Joydeep Ghosh
Efficient Output Kernel Learning for Multiple Tasks Pratik Jawanpuria, Maksim Lapin, Matthias Hein, Bernt Schiele
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models Michael C. Hughes, William T. Stephenson, Erik Sudderth
Variational Consensus Monte Carlo Maxim Rabinovich, Elaine Angelino, Michael I. Jordan
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma Murat A. Erdogdu
Practical and Optimal LSH for Angular Distance Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt
Learning to Linearize Under Uncertainty Ross Goroshin, Michael F. Mathieu, Yann LeCun
Finite-Time Analysis of Projected Langevin Monte Carlo Sebastien Bubeck, Ronen Eldan, Joseph Lehec
Deep Visual Analogy-Making Scott E. Reed, Yi Zhang, Yuting Zhang, Honglak Lee
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation Alaa Saade, Florent Krzakala, Lenka Zdeborová
Online Learning with Adversarial Delays Kent Quanrud, Daniel Khashabi
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection Jie Wang, Jieping Ye
Minimum Weight Perfect Matching via Blossom Belief Propagation Sung-Soo Ahn, Sejun Park, Michael Chertkov, Jinwoo Shin
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation Jaya Kawale, Hung H. Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla
Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems Ruoyu Sun, Mingyi Hong
Lifted Symmetry Detection and Breaking for MAP Inference Timothy Kopp, Parag Singla, Henry Kautz
Evaluating the statistical significance of biclusters Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
Discriminative Robust Transformation Learning Jiaji Huang, Qiang Qiu, Guillermo Sapiro, Robert Calderbank
Bandits with Unobserved Confounders: A Causal Approach Elias Bareinboim, Andrew Forney, Judea Pearl
Scalable Semi-Supervised Aggregation of Classifiers Akshay Balsubramani, Yoav Freund
Online Learning with Gaussian Payoffs and Side Observations Yifan Wu, András György, Csaba Szepesvari
Private Graphon Estimation for Sparse Graphs Christian Borgs, Jennifer Chayes, Adam Smith
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals Qing Sun, Dhruv Batra
Fast Second Order Stochastic Backpropagation for Variational Inference Kai Fan, Ziteng Wang, Jeff Beck, James Kwok, Katherine A. Heller
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition Cameron Musco, Christopher Musco
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada, Takeshi Yamada
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods Amir Dezfouli, Edwin V. Bonilla
Fast Bidirectional Probability Estimation in Markov Models Siddhartha Banerjee, Peter Lofgren
Probabilistic Variational Bounds for Graphical Models Qiang Liu, John W. Fisher III, Alexander T. Ihler
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes Ryan J. Giordano, Tamara Broderick, Michael I. Jordan
Combinatorial Cascading Bandits Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvari
Policy Gradient for Coherent Risk Measures Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor
Fast Rates for Exp-concave Empirical Risk Minimization Tomer Koren, Kfir Levy
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Emily L. Denton, Soumith Chintala, arthur szlam, Rob Fergus
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Seunghoon Hong, Hyeonwoo Noh, Bohyung Han
Equilibrated adaptive learning rates for non-convex optimization Yann Dauphin, Harm de Vries, Yoshua Bengio
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care Sorathan Chaturapruek, John C. Duchi, Christopher Ré
Lifelong Learning with Non-i.i.d. Tasks Anastasia Pentina, Christoph H. Lampert
Optimal Linear Estimation under Unknown Nonlinear Transform Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu
Learning with Group Invariant Features: A Kernel Perspective. Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees Xinyang Yi, Constantine Caramanis
Distributionally Robust Logistic Regression Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, Daniel Kuhn
Adaptive Stochastic Optimization: From Sets to Paths Zhan Wei Lim, David Hsu, Wee Sun Lee
Beyond Convexity: Stochastic Quasi-Convex Optimization Elad Hazan, Kfir Levy, Shai Shalev-Shwartz
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding Yuval Harel, Ron Meir, Manfred Opper
Sum-of-Squares Lower Bounds for Sparse PCA Tengyu Ma, Avi Wigderson
Max-Margin Majority Voting for Learning from Crowds TIAN TIAN, Jun Zhu
Learning with Incremental Iterative Regularization Lorenzo Rosasco, Silvia Villa
Halting in Random Walk Kernels Mahito Sugiyama, Karsten Borgwardt
MCMC for Variationally Sparse Gaussian Processes James Hensman, Alexander G. Matthews, Maurizio Filippone, Zoubin Ghahramani
Less is More: Nyström Computational Regularization Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
Infinite Factorial Dynamical Model Isabel Valera, Francisco Ruiz, Lennart Svensson, Fernando Perez-Cruz
Regularization Path of Cross-Validation Error Lower Bounds Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments Dane S. Corneil, Wulfram Gerstner
Teaching Machines to Read and Comprehend Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions Jonas W. Mueller, Tommi Jaakkola
When are Kalman-Filter Restless Bandits Indexable? Christopher R. Dance, Tomi Silander
Segregated Graphs and Marginals of Chain Graph Models Ilya Shpitser
Efficient Non-greedy Optimization of Decision Trees Mohammad Norouzi, Maxwell Collins, Matthew A. Johnson, David J. Fleet, Pushmeet Kohli
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process Ye Wang, David B. Dunson
Inverse Reinforcement Learning with Locally Consistent Reward Functions Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Communication Complexity of Distributed Convex Learning and Optimization Yossi Arjevani, Ohad Shamir
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng
Subset Selection by Pareto Optimization Chao Qian, Yang Yu, Zhi-Hua Zhou
On the Accuracy of Self-Normalized Log-Linear Models Jacob Andreas, Maxim Rabinovich, Michael I. Jordan, Dan Klein
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
Is Approval Voting Optimal Given Approval Votes? Ariel D. Procaccia, Nisarg Shah
Regressive Virtual Metric Learning Michaël Perrot, Amaury Habrard
Analysis of Robust PCA via Local Incoherence Huishuai Zhang, Yi Zhou, Yingbin Liang
Learning to Transduce with Unbounded Memory Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom
Max-Margin Deep Generative Models Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang
Spherical Random Features for Polynomial Kernels Jeffrey Pennington, Felix X. Yu, Sanjiv Kumar
Rectified Factor Networks Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter
Learning Bayesian Networks with Thousands of Variables Mauro Scanagatta, Cassio P. de Campos, Giorgio Corani, Marco Zaffalon
Matrix Completion Under Monotonic Single Index Models Ravi Sastry Ganti, Laura Balzano, Rebecca Willett
Visalogy: Answering Visual Analogy Questions Fereshteh Sadeghi, C. Lawrence Zitnick, Ali Farhadi
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models Juho Lee, Seungjin Choi
Streaming Min-max Hypergraph Partitioning Dan Alistarh, Jennifer Iglesias, Milan Vojnovic
Collaboratively Learning Preferences from Ordinal Data Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu
Biologically Inspired Dynamic Textures for Probing Motion Perception Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré
Generative Image Modeling Using Spatial LSTMs Lucas Theis, Matthias Bethge
Robust PCA with compressed data Wooseok Ha, Rina Foygel Barber
Sampling from Probabilistic Submodular Models Alkis Gotovos, Hamed Hassani, Andreas Krause
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha, Le Song
Supervised Learning for Dynamical System Learning Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon
Regret-Based Pruning in Extensive-Form Games Noam Brown, Tuomas Sandholm
Fast Two-Sample Testing with Analytic Representations of Probability Measures Kacper P. Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton
Learning to Segment Object Candidates Pedro O. Pinheiro, Ronan Collobert, Piotr Dollar
GP Kernels for Cross-Spectrum Analysis Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin
Secure Multi-party Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath
Spatial Transformer Networks Max Jaderberg, Karen Simonyan, Andrew Zisserman, koray kavukcuoglu
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms Yunwen Lei, Urun Dogan, Alexander Binder, Marius Kloft
High-dimensional neural spike train analysis with generalized count linear dynamical systems Yuanjun Gao, Lars Busing, Krishna V. Shenoy, John P. Cunningham
Learning with a Wasserstein Loss Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, Tomaso A. Poggio
b-bit Marginal Regression Martin Slawski, Ping Li
Natural Neural Networks Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, koray kavukcuoglu
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference Ted Meeds, Max Welling
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing Tom Goldstein, Min Li, Xiaoming Yuan
On some provably correct cases of variational inference for topic models Pranjal Awasthi, Andrej Risteski
Collaborative Filtering with Graph Information: Consistency and Scalable Methods Nikhil Rao, Hsiang-Fu Yu, Pradeep K. Ravikumar, Inderjit S. Dhillon
Combinatorial Bandits Revisited Richard Combes, Mohammad Sadegh Talebi Mazraeh Shahi, Alexandre Proutiere, marc lelarge
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning Shakir Mohamed, Danilo Jimenez Rezende
A Structural Smoothing Framework For Robust Graph Comparison Pinar Yanardag, S.V.N. Vishwanathan
Competitive Distribution Estimation: Why is Good-Turing Good Alon Orlitsky, Ananda Theertha Suresh
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama
A hybrid sampler for Poisson-Kingman mixture models Maria Lomeli, Stefano Favaro, Yee Whye Teh
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching Xiao Li, Kannan Ramchandran
Local Smoothness in Variance Reduced Optimization Daniel Vainsencher, Han Liu, Tong Zhang
Saliency, Scale and Information: Towards a Unifying Theory Shafin Rahman, Neil Bruce
Fighting Bandits with a New Kind of Smoothness Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs Vidyashankar Sivakumar, Arindam Banerjee, Pradeep K. Ravikumar
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction Rakesh Shivanna, Bibaswan K. Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach
Convolutional Networks on Graphs for Learning Molecular Fingerprints David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, Ryan P. Adams
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications Kai Wei, Rishabh K. Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes
Tractable Learning for Complex Probability Queries Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, Guy Van den Broeck
StopWasting My Gradients: Practical SVRG Reza Harikandeh, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konečný, Scott Sallinen
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction Been Kim, Julie A. Shah, Finale Doshi-Velez
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks Cengiz Pehlevan, Dmitri Chklovskii
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen, Nan Ding, Lawrence Carin
Learning structured densities via infinite dimensional exponential families Siqi Sun, mladen kolar, Jinbo Xu
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Haoyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu
Variance Reduced Stochastic Gradient Descent with Neighbors Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien, Brian McWilliams
Sample Efficient Path Integral Control under Uncertainty Yunpeng Pan, Evangelos Theodorou, Michail Kontitsis
Stochastic Expectation Propagation Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
Exactness of Approximate MAP Inference in Continuous MRFs Nicholas Ruozzi
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients Bo Xie, Yingyu Liang, Le Song
Generalization in Adaptive Data Analysis and Holdout Reuse Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, Aaron Roth
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents Mithun Chakraborty, Sanmay Das
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
Training Very Deep Networks Rupesh K. Srivastava, Klaus Greff, Juergen Schmidhuber
Bayesian Active Model Selection with an Application to Automated Audiometry Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham
Particle Gibbs for Infinite Hidden Markov Models Nilesh Tripuraneni, Shixiang Gu, Hong Ge, Zoubin Ghahramani
Learning spatiotemporal trajectories from manifold-valued longitudinal data Jean-Baptiste SCHIRATTI, Stéphanie ALLASSONNIERE, Olivier Colliot, Stanley DURRLEMAN
A Bayesian Framework for Modeling Confidence in Perceptual Decision Making Koosha Khalvati, Rajesh P. Rao
Path-SGD: Path-Normalized Optimization in Deep Neural Networks Behnam Neyshabur, Ruslan R. Salakhutdinov, Nati Srebro
On the consistency theory of high dimensional variable screening Xiangyu Wang, Chenlei Leng, David B. Dunson
End-To-End Memory Networks Sainbayar Sukhbaatar, arthur szlam, Jason Weston, Rob Fergus
Spectral Representations for Convolutional Neural Networks Oren Rippel, Jasper Snoek, Ryan P. Adams
Online Gradient Boosting Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo
Deep Temporal Sigmoid Belief Networks for Sequence Modeling Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin
Recognizing retinal ganglion cells in the dark Emile Richard, Georges A. Goetz, E. J. Chichilnisky
A Theory of Decision Making Under Dynamic Context Michael Shvartsman, Vaibhav Srivastava, Jonathan D. Cohen
A Gaussian Process Model of Quasar Spectral Energy Distributions Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang, Mr. Prabhat, David Schlegel, Ryan P. Adams
Hidden Technical Debt in Machine Learning Systems D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
Local Causal Discovery of Direct Causes and Effects Tian Gao, Qiang Ji
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
Revenue Optimization against Strategic Buyers Mehryar Mohri, Andres Munoz
Deep Convolutional Inverse Graphics Network Tejas D. Kulkarni, William F. Whitney, Pushmeet Kohli, Josh Tenenbaum
Sparse and Low-Rank Tensor Decomposition Parikshit Shah, Nikhil Rao, Gongguo Tang
Minimax Time Series Prediction Wouter M. Koolen, Alan Malek, Peter L. Bartlett, Yasin Abbasi
Differentially Private Learning of Structured Discrete Distributions Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma, Tim Salimans, Max Welling
Sample Complexity of Learning Mahalanobis Distance Metrics Nakul Verma, Kristin Branson
Learning Wake-Sleep Recurrent Attention Models Jimmy Ba, Ruslan R. Salakhutdinov, Roger B. Grosse, Brendan J. Frey
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso Eunho Yang, Aurelie C. Lozano
Testing Closeness With Unequal Sized Samples Bhaswar Bhattacharya, Gregory Valiant
Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach Wenye Li
Neural Adaptive Sequential Monte Carlo Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
Local Expectation Gradients for Black Box Variational Inference Michalis Titsias RC AUEB, Miguel Lázaro-Gredilla
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J. Smola
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning Kevin G. Jamieson, Lalit Jain, Chris Fernandez, Nicholas J. Glattard, Rob Nowak
Super-Resolution Off the Grid Qingqing Huang, Sham M. Kakade
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms Christopher M. De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré, Christopher Ré
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors Dan Rosenbaum, Yair Weiss
Pointer Networks Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
Associative Memory via a Sparse Recovery Model Arya Mazumdar, Ankit Singh Rawat
Robust Spectral Inference for Joint Stochastic Matrix Factorization Moontae Lee, David Bindel, David Mimno
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms Rasmus Kyng, Anup Rao, Sushant Sachdeva
Adversarial Prediction Games for Multivariate Losses Hong Wang, Wei Xing, Kaiser Asif, Brian Ziebart
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization Xiangru Lian, Yijun Huang, Yuncheng Li, Ji Liu
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images Manuel Watter, Jost Springenberg, Joschka Boedecker, Martin Riedmiller
Efficient and Parsimonious Agnostic Active Learning Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
Softstar: Heuristic-Guided Probabilistic Inference Mathew Monfort, Brenden M. Lake, Brenden M. Lake, Brian Ziebart, Patrick Lucey, Josh Tenenbaum
Grammar as a Foreign Language Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton
Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices Martin Slawski, Ping Li, Matthias Hein
Winner-Take-All Autoencoders Alireza Makhzani, Brendan J. Frey
Deep Poisson Factor Modeling Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin
Bayesian Optimization with Exponential Convergence Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning Christoph Dann, Emma Brunskill
Learning with Relaxed Supervision Jacob Steinhardt, Percy S. Liang
Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's Vitaly Feldman, Will Perkins, Santosh Vempala
Accelerated Mirror Descent in Continuous and Discrete Time Walid Krichene, Alexandre Bayen, Peter L. Bartlett
The Human Kernel Andrew G. Wilson, Christoph Dann, Chris Lucas, Eric P. Xing
Action-Conditional Video Prediction using Deep Networks in Atari Games Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder Singh
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA James R. Voss, Mikhail Belkin, Luis Rademacher
Distributed Submodular Cover: Succinctly Summarizing Massive Data Baharan Mirzasoleiman, Amin Karbasi, Ashwinkumar Badanidiyuru, Andreas Krause
Community Detection via Measure Space Embedding Mark Kozdoba, Shie Mannor
Basis refinement strategies for linear value function approximation in MDPs Gheorghe Comanici, Doina Precup, Prakash Panangaden
Structured Estimation with Atomic Norms: General Bounds and Applications Sheng Chen, Arindam Banerjee
A Complete Recipe for Stochastic Gradient MCMC Yi-An Ma, Tianqi Chen, Emily Fox
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff Ofer Dekel, Ronen Eldan, Tomer Koren
Online Prediction at the Limit of Zero Temperature Mark Herbster, Stephen Pasteris, Shaona Ghosh
Learning Continuous Control Policies by Stochastic Value Gradients Nicolas Heess, Gregory Wayne, David Silver, Tim Lillicrap, Tom Erez, Yuval Tassa
Exploring Models and Data for Image Question Answering Mengye Ren, Ryan Kiros, Richard Zemel
Efficient and Robust Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter
Preconditioned Spectral Descent for Deep Learning David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher
A Recurrent Latent Variable Model for Sequential Data Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio
Fast Convergence of Regularized Learning in Games Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Juergen Schmidhuber
Reflection, Refraction, and Hamiltonian Monte Carlo Hadi Mohasel Afshar, Justin Domke
The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels Purnamrita Sarkar, Deepayan Chakrabarti, peter j. bickel
Nearly Optimal Private LASSO Kunal Talwar, Abhradeep Thakurta, Li Zhang
Convergence Analysis of Prediction Markets via Randomized Subspace Descent Rafael Frongillo, Mark D. Reid
The Poisson Gamma Belief Network Mingyuan Zhou, Yulai Cong, Bo Chen
Convergence rates of sub-sampled Newton methods Murat A. Erdogdu, Andrea Montanari
No-Regret Learning in Bayesian Games Jason Hartline, Vasilis Syrgkanis, Eva Tardos
Statistical Topological Data Analysis - A Kernel Perspective Roland Kwitt, Stefan Huber, Marc Niethammer, Weili Lin, Ulrich Bauer
Semi-supervised Sequence Learning Andrew M. Dai, Quoc V. Le
Structured Transforms for Small-Footprint Deep Learning Vikas Sindhwani, Tara Sainath, Sanjiv Kumar
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width Christopher M. De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm Qinqing Zheng, Ryota Tomioka
Sample Complexity Bounds for Iterative Stochastic Policy Optimization Marin Kobilarov
BinaryConnect: Training Deep Neural Networks with binary weights during propagations Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
Interactive Control of Diverse Complex Characters with Neural Networks Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, Emanuel V. Todorov
Submodular Hamming Metrics Jennifer A. Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes
A Universal Primal-Dual Convex Optimization Framework Alp Yurtsever, Quoc Tran Dinh, Volkan Cevher
Learning From Small Samples: An Analysis of Simple Decision Heuristics Özgür Şimşek, Marcus Buckmann
Explore no more: Improved high-probability regret bounds for non-stochastic bandits Gergely Neu
Fast and Memory Optimal Low-Rank Matrix Approximation Se-Young Yun, marc lelarge, Alexandre Proutiere
Learnability of Influence in Networks Harikrishna Narasimhan, David C. Parkes, Yaron Singer
Learning Causal Graphs with Small Interventions Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath
Information-theoretic lower bounds for convex optimization with erroneous oracles Yaron Singer, Jan Vondrak
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial David I. Inouye, Pradeep K. Ravikumar, Inderjit S. Dhillon
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin
The Self-Normalized Estimator for Counterfactual Learning Adith Swaminathan, Thorsten Joachims
Fast Lifted MAP Inference via Partitioning Somdeb Sarkhel, Parag Singla, Vibhav G. Gogate
Data Generation as Sequential Decision Making Philip Bachman, Doina Precup
On Elicitation Complexity Rafael Frongillo, Ian Kash
Decomposition Bounds for Marginal MAP Wei Ping, Qiang Liu, Alexander T. Ihler
Discrete Rényi Classifiers Meisam Razaviyayn, Farzan Farnia, David Tse
A class of network models recoverable by spectral clustering Yali Wan, Marina Meila
Skip-Thought Vectors Ryan Kiros, Yukun Zhu, Ruslan R. Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, Sanja Fidler
Rate-Agnostic (Causal) Structure Learning Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun
Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric Vivien Seguy, Marco Cuturi
Consistent Multilabel Classification Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions Amar Shah, Zoubin Ghahramani
Cornering Stationary and Restless Mixing Bandits with Remix-UCB Julien Audiffren, Liva Ralaivola
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Gael Varoquaux
Gaussian Process Random Fields David Moore, Stuart J. Russell
M-Statistic for Kernel Change-Point Detection Shuang Li, Yao Xie, Hanjun Dai, Le Song
Adaptive Online Learning Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
A Universal Catalyst for First-Order Optimization Hongzhou Lin, Julien Mairal, Zaid Harchaoui
Inference for determinantal point processes without spectral knowledge Rémi Bardenet, Michalis Titsias RC AUEB
Kullback-Leibler Proximal Variational Inference Mohammad E. Khan, Pierre Baque, François Fleuret, Pascal Fua
Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization Niao He, Zaid Harchaoui
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements CHRISTOS THRAMPOULIDIS, Ehsan Abbasi, Babak Hassibi
From random walks to distances on unweighted graphs Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola
Bayesian dark knowledge Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, Max Welling
Matrix Completion with Noisy Side Information Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation Scott Linderman, Matthew Johnson, Ryan P. Adams
On-the-Job Learning with Bayesian Decision Theory Keenon Werling, Arun Tejasvi Chaganty, Percy S. Liang, Christopher D. Manning
Calibrated Structured Prediction Volodymyr Kuleshov, Percy S. Liang
Learning Structured Output Representation using Deep Conditional Generative Models Kihyuk Sohn, Honglak Lee, Xinchen Yan
Time-Sensitive Recommendation From Recurrent User Activities Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels Felipe Tobar, Thang D. Bui, Richard E. Turner
A Market Framework for Eliciting Private Data Bo Waggoner, Rafael Frongillo, Jacob D. Abernethy
Lifted Inference Rules With Constraints Happy Mittal, Anuj Mahajan, Vibhav G. Gogate, Parag Singla
Gradient Estimation Using Stochastic Computation Graphs John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel
Model-Based Relative Entropy Stochastic Search Abbas Abdolmaleki, Rudolf Lioutikov, Jan R. Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann
Semi-supervised Learning with Ladder Networks Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, Tapani Raiko
Embedding Inference for Structured Multilabel Prediction Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans
Copula variational inference Dustin Tran, David Blei, Edo M. Airoldi
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction Kisuk Lee, Aleksandar Zlateski, Vishwanathan Ashwin, H. Sebastian Seung
A Dual Augmented Block Minimization Framework for Learning with Limited Memory Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin
Optimal Testing for Properties of Distributions Jayadev Acharya, Constantinos Daskalakis, Gautam C. Kamath
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M. Rehg
Expectation Particle Belief Propagation Thibaut Lienart, Yee Whye Teh, Arnaud Doucet
Latent Bayesian melding for integrating individual and population models Mingjun Zhong, Nigel Goddard, Charles Sutton

Advances in Neural Information Processing Systems 29 (NIPS 2016)

The papers below appear in Advances in Neural Information Processing Systems 29 edited by D.D. Lee and M. Sugiyama and U.V. Luxburg and I. Guyon and R. Garnett.
They are proceedings from the conference, "Neural Information Processing Systems 2016."

Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much Bryan D. He, Christopher M. De Sa, Ioannis Mitliagkas, Christopher Ré
Deep ADMM-Net for Compressive Sensing MRI yan yang, Jian Sun, Huibin Li, Zongben Xu
A scaled Bregman theorem with applications Richard Nock, Aditya Menon, Cheng Soon Ong
Swapout: Learning an ensemble of deep architectures Saurabh Singh, Derek Hoiem, David Forsyth
On Regularizing Rademacher Observation Losses Richard Nock
Without-Replacement Sampling for Stochastic Gradient Methods Ohad Shamir
Fast and Provably Good Seedings for k-Means Olivier Bachem, Mario Lucic, Hamed Hassani, Andreas Krause
Unsupervised Learning for Physical Interaction through Video Prediction Chelsea Finn, Ian Goodfellow, Sergey Levine
High-Rank Matrix Completion and Clustering under Self-Expressive Models Ehsan Elhamifar
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, Josh Tenenbaum
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks Tianfan Xue, Jiajun Wu, Katherine Bouman, Bill Freeman
Human Decision-Making under Limited Time Pedro A. Ortega, Alan A. Stocker
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition Shizhong Han, Zibo Meng, AHMED-SHEHAB KHAN, Yan Tong
Natural-Parameter Networks: A Class of Probabilistic Neural Networks Hao Wang, Xingjian SHI, Dit-Yan Yeung
Tree-Structured Reinforcement Learning for Sequential Object Localization Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Lu, Shuicheng Yan
Unsupervised Domain Adaptation with Residual Transfer Networks Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
Verification Based Solution for Structured MAB Problems Zohar S. Karnin
Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre Bayen
Linear dynamical neural population models through nonlinear embeddings Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham
SURGE: Surface Regularized Geometry Estimation from a Single Image Peng Wang, Xiaohui Shen, Bryan Russell, Scott Cohen, Brian Price, Alan L. Yuille
Interpretable Distribution Features with Maximum Testing Power Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton
Sorting out typicality with the inverse moment matrix SOS polynomial Edouard Pauwels, Jean B. Lasserre
Multi-armed Bandits: Competing with Optimal Sequences Zohar S. Karnin, Oren Anava
Multivariate tests of association based on univariate tests Ruth Heller, Yair Heller
Learning What and Where to Draw Scott E. Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee
The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM Damek Davis, Brent Edmunds, Madeleine Udell
Integrated perception with recurrent multi-task neural networks Hakan Bilen, Andrea Vedaldi
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs Yu-Xiong Wang, Martial Hebert
CNNpack: Packing Convolutional Neural Networks in the Frequency Domain Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, Chao Xu
Cooperative Graphical Models Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Sebastian Nowozin, Botond Cseke, Ryota Tomioka
Bayesian Optimization for Probabilistic Programs Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank Wood
Hierarchical Question-Image Co-Attention for Visual Question Answering Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh
Optimal Sparse Linear Encoders and Sparse PCA Malik Magdon-Ismail, Christos Boutsidis
FPNN: Field Probing Neural Networks for 3D Data Yangyan Li, Soeren Pirk, Hao Su, Charles R. Qi, Leonidas J. Guibas
CRF-CNN: Modeling Structured Information in Human Pose Estimation Xiao Chu, Wanli Ouyang, hongsheng Li, Xiaogang Wang
Fairness in Learning: Classic and Contextual Bandits Matthew Joseph, Michael Kearns, Jamie H. Morgenstern, Aaron Roth
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy
Domain Separation Networks Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan
DISCO Nets : DISsimilarity COefficients Networks Diane Bouchacourt, Pawan K. Mudigonda, Sebastian Nowozin
Multimodal Residual Learning for Visual QA Jin-Hwa Kim, Sang-Woo Lee, Donghyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang
CMA-ES with Optimal Covariance Update and Storage Complexity Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel
R-FCN: Object Detection via Region-based Fully Convolutional Networks Jifeng Dai, Yi Li, Kaiming He, Jian Sun
GAP Safe Screening Rules for Sparse-Group Lasso Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Learning and Forecasting Opinion Dynamics in Social Networks Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez Rodriguez
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares Rong Zhu
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks Hao Wang, Xingjian SHI, Dit-Yan Yeung
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula Jean Barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborová
A Unified Approach for Learning the Parameters of Sum-Product Networks Han Zhao, Pascal Poupart, Geoffrey J. Gordon
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images Junhua Mao, Jiajing Xu, Kevin Jing, Alan L. Yuille
Stochastic Online AUC Maximization Yiming Ying, Longyin Wen, Siwei Lyu
The Generalized Reparameterization Gradient Francisco R. Ruiz, Michalis Titsias RC AUEB, David Blei
Coupled Generative Adversarial Networks Ming-Yu Liu, Oncel Tuzel
Exponential Family Embeddings Maja Rudolph, Francisco Ruiz, Stephan Mandt, David Blei
Variational Information Maximization for Feature Selection Shuyang Gao, Greg Ver Steeg, Aram Galstyan
Operator Variational Inference Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David Blei
Fast learning rates with heavy-tailed losses Vu C. Dinh, Lam S. Ho, Binh Nguyen, Duy Nguyen
Budgeted stream-based active learning via adaptive submodular maximization Kaito Fujii, Hisashi Kashima
Learning feed-forward one-shot learners Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip Torr, Andrea Vedaldi
Learning User Perceived Clusters with Feature-Level Supervision Ting-Yu Cheng, Guiguan Lin, xinyang gong, Kang-Jun Liu, Shan-Hung Wu
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization Pan Zhang
Residual Networks Behave Like Ensembles of Relatively Shallow Networks Andreas Veit, Michael J. Wilber, Serge Belongie
Adversarial Multiclass Classification: A Risk Minimization Perspective Rizal Fathony, Anqi Liu, Kaiser Asif, Brian Ziebart
Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow Gang Wang, Georgios Giannakis
Coin Betting and Parameter-Free Online Learning Francesco Orabona, David Pal
Deep Learning without Poor Local Minima Kenji Kawaguchi
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko
A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ Dennis Wei
Generating Videos with Scene Dynamics Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah Goodman
A Powerful Generative Model Using Random Weights for the Deep Image Representation Kun He, Yan Wang, John Hopcroft
Optimizing affinity-based binary hashing using auxiliary coordinates Ramin Raziperchikolaei, Miguel A. Carreira-Perpinan
Double Thompson Sampling for Dueling Bandits Huasen Wu, Xin Liu
Generating Images with Perceptual Similarity Metrics based on Deep Networks Alexey Dosovitskiy, Thomas Brox
Dynamic Filter Networks Xu Jia, Bert De Brabandere, Tinne Tuytelaars, Luc V. Gool
A Simple Practical Accelerated Method for Finite Sums Aaron Defazio
Barzilai-Borwein Step Size for Stochastic Gradient Descent Conghui Tan, Shiqian Ma, Yu-Hong Dai, Yuqiu Qian
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability Guillaume Papa, Aurélien Bellet, Stephan Clémençon
Optimal spectral transportation with application to music transcription Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya
Regularized Nonlinear Acceleration Damien Scieur, Alexandre d'Aspremont, Francis Bach
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros
Single-Image Depth Perception in the Wild Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng
Computational and Statistical Tradeoffs in Learning to Rank Ashish Khetan, Sewoong Oh
Online Convex Optimization with Unconstrained Domains and Losses Ashok Cutkosky, Kwabena A. Boahen
An ensemble diversity approach to supervised binary hashing Miguel A. Carreira-Perpinan, Ramin Raziperchikolaei
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis Weiran Wang, Jialei Wang, Dan Garber, Dan Garber, Nati Srebro
The Power of Adaptivity in Identifying Statistical Alternatives Kevin G. Jamieson, Daniel Haas, Benjamin Recht
On Explore-Then-Commit strategies Aurelien Garivier, Tor Lattimore, Emilie Kaufmann
Sublinear Time Orthogonal Tensor Decomposition Zhao Song, David Woodruff, Huan Zhang
DECOrrelated feature space partitioning for distributed sparse regression Xiangyu Wang, David B. Dunson, Chenlei Leng
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition Jinzhuo Wang, Wenmin Wang, xiongtao Chen, Ronggang Wang, Wen Gao
Dual Learning for Machine Translation Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tieyan Liu, Wei-Ying Ma
Dialog-based Language Learning Jason E. Weston
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition Theodore Bluche
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon
Active Nearest-Neighbor Learning in Metric Spaces Aryeh Kontorovich, Sivan Sabato, Ruth Urner
Proximal Deep Structured Models Shenlong Wang, Sanja Fidler, Raquel Urtasun
Faster Projection-free Convex Optimization over the Spectrahedron Dan Garber, Dan Garber
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach Remi Lam, Karen Willcox, David H. Wolpert
SoundNet: Learning Sound Representations from Unlabeled Video Yusuf Aytar, Carl Vondrick, Antonio Torralba
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Tim Salimans, Diederik P. Kingma
Efficient Second Order Online Learning by Sketching Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis Yoshinobu Kawahara
Distributed Flexible Nonlinear Tensor Factorization Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani
The Robustness of Estimator Composition Pingfan Tang, Jeff M. Phillips
Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats Pulkit Tandon, Yash H. Malviya, Bipin Rajendran
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Mikhail Figurnov, Aizhan Ibraimova, Dmitry P. Vetrov, Pushmeet Kohli
Differential Privacy without Sensitivity Kentaro Minami, HItomi Arai, Issei Sato, Hiroshi Nakagawa
Optimal Cluster Recovery in the Labeled Stochastic Block Model Se-Young Yun, Alexandre Proutiere
LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain Zeyuan Allen-Zhu, Yuanzhi Li
An algorithm for L1 nearest neighbor search via monotonic embedding Xinan Wang, Sanjoy Dasgupta
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff Schneider, Barnabas Poczos
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes Dan Garber, Dan Garber, Ofer Meshi
Efficient Nonparametric Smoothness Estimation Shashank Singh, Simon S. Du, Barnabas Poczos
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Yarin Gal, Zoubin Ghahramani
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios, Iain Murray
Direct Feedback Alignment Provides Learning in Deep Neural Networks Arild Nøkland
Safe and Efficient Off-Policy Reinforcement Learning Remi Munos, Tom Stepleton, Anna Harutyunyan, Marc Bellemare
A Multi-Batch L-BFGS Method for Machine Learning Albert S. Berahas, Jorge Nocedal, Martin Takac
Semiparametric Differential Graph Models Pan Xu, Quanquan Gu
Rényi Divergence Variational Inference Yingzhen Li, Richard E. Turner
Doubly Convolutional Neural Networks Shuangfei Zhai, Yu Cheng, Zhongfei (Mark) Zhang, Weining Lu
Density Estimation via Discrepancy Based Adaptive Sequential Partition Dangna Li, Kun Yang, Wing Hung Wong
How Deep is the Feature Analysis underlying Rapid Visual Categorization? Sven Eberhardt, Jonah G. Cader, Thomas Serre
VIME: Variational Information Maximizing Exploration Rein Houthooft, Xi Chen, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain Timothy Rubin, Oluwasanmi O. Koyejo, Michael N. Jones, Tal Yarkoni
Solving Marginal MAP Problems with NP Oracles and Parity Constraints Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman
Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models Tomoharu Iwata, Makoto Yamada
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J. Smola
Variance Reduction in Stochastic Gradient Langevin Dynamics Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabas Poczos, Alexander J. Smola, Eric P. Xing
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen
Dense Associative Memory for Pattern Recognition Dmitry Krotov, John J. Hopfield
Causal Bandits: Learning Good Interventions via Causal Inference Finnian Lattimore, Tor Lattimore, Mark D. Reid
Refined Lower Bounds for Adversarial Bandits Sébastien Gerchinovitz, Tor Lattimore
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon) Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators Shashank Singh, Barnabas Poczos
A state-space model of cross-region dynamic connectivity in MEG/EEG Ying Yang, Elissa Aminoff, Michael Tarr, Kass E. Robert
What Makes Objects Similar: A Unified Multi-Metric Learning Approach Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint Nguyen Cuong, Huan Xu
Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions Siddartha Y. Ramamohan, Arun Rajkumar, Shivani Agarwal, Shivani Agarwal
Local Similarity-Aware Deep Feature Embedding Chen Huang, Chen Change Loy, Xiaoou Tang
A Communication-Efficient Parallel Algorithm for Decision Tree Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tieyan Liu
Convex Two-Layer Modeling with Latent Structure Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen
Sampling for Bayesian Program Learning Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
Learning Kernels with Random Features Aman Sinha, John C. Duchi
Optimal Tagging with Markov Chain Optimization Nir Rosenfeld, Amir Globerson
Crowdsourced Clustering: Querying Edges vs Triangles Ramya Korlakai Vinayak, Babak Hassibi
Mixed vine copulas as joint models of spike counts and local field potentials Arno Onken, Stefano Panzeri
Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation Emmanuel Abbe, Colin Sandon
Adaptive Concentration Inequalities for Sequential Decision Problems Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
Nested Mini-Batch K-Means James Newling, François Fleuret
Deep Learning Models of the Retinal Response to Natural Scenes Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus
Preference Completion from Partial Rankings Suriya Gunasekar, Oluwasanmi O. Koyejo, Joydeep Ghosh
Dynamic Network Surgery for Efficient DNNs Yiwen Guo, Anbang Yao, Yurong Chen
Learning a Metric Embedding for Face Recognition using the Multibatch Method Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua
A Pseudo-Bayesian Algorithm for Robust PCA Tae-Hyun Oh, Yasuyuki Matsushita, In Kweon, David Wipf
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks Julien Mairal
Stochastic Variance Reduction Methods for Saddle-Point Problems Balamurugan Palaniappan, Francis Bach
Flexible Models for Microclustering with Application to Entity Resolution Brenda Betancourt, Giacomo Zanella, Jeffrey W. Miller, Hanna Wallach, Abbas Zaidi, Rebecca C. Steorts
Catching heuristics are optimal control policies Boris Belousov, Gerhard Neumann, Constantin A. Rothkopf, Jan R. Peters
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian Victor Picheny, Robert B. Gramacy, Stefan Wild, Sebastien Le Digabel
Adaptive Neural Compilation Rudy R. Bunel, Alban Desmaison, Pawan K. Mudigonda, Pushmeet Kohli, Philip Torr
Synthesis of MCMC and Belief Propagation Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos, Marco Zaffalon
Unifying Count-Based Exploration and Intrinsic Motivation Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Remi Munos
Large Margin Discriminant Dimensionality Reduction in Prediction Space Mohammad Saberian, Jose Costa Pereira, Can Xu, Jian Yang, Nuno Nvasconcelos
Stochastic Structured Prediction under Bandit Feedback Artem Sokolov, Julia Kreutzer, Stefan Riezler, Christopher Lo
Simple and Efficient Weighted Minwise Hashing Anshumali Shrivastava
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher
Structured Sparse Regression via Greedy Hard Thresholding Prateek Jain, Nikhil Rao, Inderjit S. Dhillon
Understanding Probabilistic Sparse Gaussian Process Approximations Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky
Generating Long-term Trajectories Using Deep Hierarchical Networks Stephan Zheng, Yisong Yue, Jennifer Hobbs
Learning Tree Structured Potential Games Vikas Garg, Tommi Jaakkola
Observational-Interventional Priors for Dose-Response Learning Ricardo Silva
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Steven Z. Wu
Identification and Overidentification of Linear Structural Equation Models Bryant Chen
Adaptive Skills Adaptive Partitions (ASAP) Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
Multiple-Play Bandits in the Position-Based Model Paul Lagrée, Claire Vernade, Olivier Cappe
Optimal Black-Box Reductions Between Optimization Objectives Zeyuan Allen-Zhu, Elad Hazan
On Valid Optimal Assignment Kernels and Applications to Graph Classification Nils M. Kriege, Pierre-Louis Giscard, Richard Wilson
Robustness of classifiers: from adversarial to random noise Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing Ming Lin, Jieping Ye
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters Zeyuan Allen-Zhu, Yang Yuan, Karthik Sridharan
Combinatorial Multi-Armed Bandit with General Reward Functions Wei Chen, Wei Hu, Fu Li, Jian Li, Yu Liu, Pinyan Lu
Boosting with Abstention Corinna Cortes, Giulia DeSalvo, Mehryar Mohri
Regret of Queueing Bandits Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai
Deep Learning Games Dale Schuurmans, Martin A. Zinkevich
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods Antoine Gautier, Quynh N. Nguyen, Matthias Hein
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, Honglak Lee
A Credit Assignment Compiler for Joint Prediction Kai-Wei Chang, He He, Stephane Ross, Hal Daume III, John Langford
Accelerating Stochastic Composition Optimization Mengdi Wang, Ji Liu, Ethan Fang
Reward Augmented Maximum Likelihood for Neural Structured Prediction Mohammad Norouzi, Samy Bengio, zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans
Consistent Kernel Mean Estimation for Functions of Random Variables Adam Scibior, Carl-Johann Simon-Gabriel, Ilya O. Tolstikhin, Prof. Bernhard Schölkopf
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin
Scalable Adaptive Stochastic Optimization Using Random Projections Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
Variational Inference in Mixed Probabilistic Submodular Models Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated Namrata Vaswani, Han Guo
The Multi-fidelity Multi-armed Bandit Kirthevasan Kandasamy, Gautam Dasarathy, Barnabas Poczos, Jeff Schneider
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm Kejun Huang, Xiao Fu, Nikolaos D. Sidiropoulos
Bootstrap Model Aggregation for Distributed Statistical Learning JUN HAN, Qiang Liu
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification Steven Cheng-Xian Li, Benjamin M. Marlin
A Bandit Framework for Strategic Regression Yang Liu, Yiling Chen
Architectural Complexity Measures of Recurrent Neural Networks Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan R. Salakhutdinov, Yoshua Bengio
Statistical Inference for Cluster Trees Jisu KIM, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman
PAC Reinforcement Learning with Rich Observations Akshay Krishnamurthy, Alekh Agarwal, John Langford
Improved Deep Metric Learning with Multi-class N-pair Loss Objective Kihyuk Sohn
Unsupervised Learning of Spoken Language with Visual Context David Harwath, Antonio Torralba, James Glass
Low-Rank Regression with Tensor Responses Guillaume Rabusseau, Hachem Kadri
PAC-Bayesian Theory Meets Bayesian Inference Pascal Germain, Francis Bach, Alexandre Lacoste, Simon Lacoste-Julien
Data Poisoning Attacks on Factorization-Based Collaborative Filtering Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik
Learned Region Sparsity and Diversity Also Predicts Visual Attention Zijun Wei, Hossein Adeli, Minh Hoai, Greg Zelinsky, Dimitris Samaras
End-to-End Goal-Driven Web Navigation Rodrigo Nogueira, Kyunghyun Cho
Automated scalable segmentation of neurons from multispectral images Uygar Sümbül, Douglas Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward Boyden, Liam Paninski
Privacy Odometers and Filters: Pay-as-you-Go Composition Ryan M. Rogers, Aaron Roth, Jonathan Ullman, Salil Vadhan
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Prof. Bernhard Schölkopf
Adaptive optimal training of animal behavior Ji Hyun Bak, Jung Yoon Choi, Athena Akrami, Ilana Witten, Jonathan W. Pillow
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition Seyed Hamidreza Kasaei, Ana Maria Tomé, Luís Seabra Lopes
Relevant sparse codes with variational information bottleneck Matthew Chalk, Olivier Marre, Gasper Tkacik
Combinatorial Energy Learning for Image Segmentation Jeremy B. Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel
Orthogonal Random Features Felix X. Yu, Ananda Theertha Suresh, Krzysztof M. Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar
Fast Active Set Methods for Online Spike Inference from Calcium Imaging Johannes Friedrich, Liam Paninski
Diffusion-Convolutional Neural Networks James Atwood, Don Towsley
Bayesian latent structure discovery from multi-neuron recordings Scott Linderman, Ryan P. Adams, Jonathan W. Pillow
A Probabilistic Programming Approach To Probabilistic Data Analysis Feras Saad, Vikash K. Mansinghka
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics William Hoiles, Mihaela Van Der Schaar
Inference by Reparameterization in Neural Population Codes Rajkumar Vasudeva Raju, Xaq Pitkow
Tensor Switching Networks Chuan-Yung Tsai, Andrew M. Saxe, Andrew M. Saxe, David Cox
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël RICHARD
Coordinate-wise Power Method Qi Lei, Kai Zhong, Inderjit S. Dhillon
Learning Influence Functions from Incomplete Observations Xinran He, Ke Xu, David Kempe, Yan Liu
Learning Structured Sparsity in Deep Neural Networks Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li
Sample Complexity of Automated Mechanism Design Maria-Florina F. Balcan, Tuomas Sandholm, Ellen Vitercik
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products Sanghamitra Dutta, Viveck Cadambe, Pulkit Grover
Brains on Beats Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel van Gerven, Marcel A. J. van Gerven
Learning Transferrable Representations for Unsupervised Domain Adaptation Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra
Active Learning from Imperfect Labelers Songbai Yan, Kamalika Chaudhuri, Tara Javidi
Learning to Communicate with Deep Multi-Agent Reinforcement Learning Jakob Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson
Value Iteration Networks Aviv Tamar, YI WU, Garrett Thomas, Sergey Levine, Pieter Abbeel
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah
On the Recursive Teaching Dimension of VC Classes Xi Chen, Xi Chen, Yu Cheng, Bo Tang
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen, Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
Hardness of Online Sleeping Combinatorial Optimization Problems Satyen Kale, Chansoo Lee, David Pal
Mixed Linear Regression with Multiple Components Kai Zhong, Prateek Jain, Inderjit S. Dhillon
Sequential Neural Models with Stochastic Layers Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences Hongseok Namkoong, John C. Duchi
Minimizing Quadratic Functions in Constant Time Kohei Hayashi, Yuichi Yoshida
Improved Techniques for Training GANs Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, Xi Chen
DeepMath - Deep Sequence Models for Premise Selection Geoffrey Irving, Christian Szegedy, Alexander A. Alemi, Niklas Een, Francois Chollet, Josef Urban
Learning Multiagent Communication with Backpropagation Sainbayar Sukhbaatar, arthur szlam, Rob Fergus
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity Amit Daniely, Roy Frostig, Yoram Singer
Learning the Number of Neurons in Deep Networks Jose M. Alvarez, Mathieu Salzmann
Finding significant combinations of features in the presence of categorical covariates Laetitia Papaxanthos, Felipe Llinares-Lopez, Dean Bodenham, Karsten Borgwardt
Examples are not enough, learn to criticize! Criticism for Interpretability Been Kim, Rajiv Khanna, Oluwasanmi O. Koyejo
Optimistic Bandit Convex Optimization Scott Yang, Mehryar Mohri
Safe Policy Improvement by Minimizing Robust Baseline Regret Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow
Graphons, mergeons, and so on! Justin Eldridge, Mikhail Belkin, Yusu Wang
Hierarchical Clustering via Spreading Metrics Aurko Roy, Sebastian Pokutta
Learning Bayesian networks with ancestral constraints Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, Adnan Darwiche
Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph Wang, Venkatesh Saligrama
Clustering with Bregman Divergences: an Asymptotic Analysis Chaoyue Liu, Mikhail Belkin
Variational Autoencoder for Deep Learning of Images, Labels and Captions Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, Lawrence Carin
Review Networks for Caption Generation Zhilin Yang, Ye Yuan, Yuexin Wu, William W. Cohen, Ruslan R. Salakhutdinov
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu, Dilin Wang
A Bio-inspired Redundant Sensing Architecture Anh Tuan Nguyen, Jian Xu, Zhi Yang
Contextual semibandits via supervised learning oracles Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik
Blind Attacks on Machine Learners Alex Beatson, Zhaoran Wang, Han Liu
Universal Correspondence Network Christopher B. Choy, JunYoung Gwak, Silvio Savarese, Manmohan Chandraker
Satisfying Real-world Goals with Dataset Constraints Gabriel Goh, Andrew Cotter, Maya Gupta, Michael P. Friedlander
Deep Learning for Predicting Human Strategic Behavior Jason S. Hartford, James R. Wright, Kevin Leyton-Brown
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games Sougata Chaudhuri, Ambuj Tewari
Eliciting Categorical Data for Optimal Aggregation Chien-Ju Ho, Rafael Frongillo, Yiling Chen
Measuring the reliability of MCMC inference with bidirectional Monte Carlo Roger B. Grosse, Siddharth Ancha, Daniel M. Roy
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation Weihao Gao, Sewoong Oh, Pramod Viswanath
Selective inference for group-sparse linear models Fan Yang, Rina Foygel Barber, Prateek Jain, John Lafferty
Graph Clustering: Block-models and model free results Yali Wan, Marina Meila
Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution Christopher Lynn, Daniel D. Lee
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease Hao Zhou, Vamsi K. Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C. Johnson
Geometric Dirichlet Means Algorithm for topic inference Mikhail Yurochkin, XuanLong Nguyen
Structured Prediction Theory Based on Factor Graph Complexity Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang
Improved Dropout for Shallow and Deep Learning Zhe Li, Boqing Gong, Tianbao Yang
Constraints Based Convex Belief Propagation Yaniv Tenzer, Alex Schwing, Kevin Gimpel, Tamir Hazan
Error Analysis of Generalized Nyström Kernel Regression Hong Chen, Haifeng Xia, Heng Huang, Weidong Cai
A Probabilistic Framework for Deep Learning Ankit B. Patel, Minh Tan Nguyen, Richard Baraniuk
General Tensor Spectral Co-clustering for Higher-Order Data Tao Wu, Austin R. Benson, David F. Gleich
Cyclades: Conflict-free Asynchronous Machine Learning Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré
Single Pass PCA of Matrix Products Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis
Stochastic Variational Deep Kernel Learning Andrew G. Wilson, Zhiting Hu, Ruslan R. Salakhutdinov, Eric P. Xing
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models Marc Vuffray, Sidhant Misra, Andrey Lokhov, Michael Chertkov
Long-term Causal Effects via Behavioral Game Theory Panagiotis Toulis, David C. Parkes
Measuring Neural Net Robustness with Constraints Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, Antonio Criminisi
Reshaped Wirtinger Flow for Solving Quadratic System of Equations Huishuai Zhang, Yingbin Liang
Nearly Isometric Embedding by Relaxation James McQueen, Marina Meila, Dominique Joncas
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models Kevin Winner, Daniel R. Sheldon
Causal meets Submodular: Subset Selection with Directed Information Yuxun Zhou, Costas J. Spanos
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions Ayan Chakrabarti, Jingyu Shao, Greg Shakhnarovich
Deep Neural Networks with Inexact Matching for Person Re-Identification Arulkumar Subramaniam, Moitreya Chatterjee, Anurag Mittal
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians Ji Xu, Daniel J. Hsu, Arian Maleki
Estimating the class prior and posterior from noisy positives and unlabeled data Shantanu Jain, Martha White, Predrag Radivojac
Kronecker Determinantal Point Processes Zelda E. Mariet, Suvrit Sra
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding Lalit Jain, Kevin G. Jamieson, Rob Nowak
Feature-distributed sparse regression: a screen-and-clean approach Jiyan Yang, Michael W. Mahoney, Michael Saunders, Yuekai Sun
Learning Bound for Parameter Transfer Learning Wataru Kumagai
Learning under uncertainty: a comparison between R-W and Bayesian approach He Huang, Martin Paulus
Bi-Objective Online Matching and Submodular Allocations Hossein Esfandiari, Nitish Korula, Vahab Mirrokni
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity Ping Li, Michael Mitzenmacher, Martin Slawski
The non-convex Burer-Monteiro approach works on smooth semidefinite programs Nicolas Boumal, Vlad Voroninski, Afonso Bandeira
Dimensionality Reduction of Massive Sparse Datasets Using Coresets Dan Feldman, Mikhail Volkov, Daniela Rus
Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model Zhen Xu, Wen Dong, Sargur N. Srihari
Supervised learning through the lens of compression Ofir David, Shay Moran, Amir Yehudayoff
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data Xinghua Lou, Ken Kansky, Wolfgang Lehrach, CC Laan, Bhaskara Marthi, D. Phoenix, Dileep George
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections Xiaojiao Mao, Chunhua Shen, Yu-Bin Yang
Object based Scene Representations using Fisher Scores of Local Subspace Projections Mandar D. Dixit, Nuno Vasconcelos
Active Learning with Oracle Epiphany Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu
Statistical Inference for Pairwise Graphical Models Using Score Matching Ming Yu, Mladen Kolar, Varun Gupta
Improved Error Bounds for Tree Representations of Metric Spaces Samir Chowdhury, Facundo Mémoli, Zane T. Smith
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes? Arturo Deza, Miguel Eckstein
On Multiplicative Integration with Recurrent Neural Networks Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan R. Salakhutdinov
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing
Regret Bounds for Non-decomposable Metrics with Missing Labels Nagarajan Natarajan, Prateek Jain
Robust k-means: a Theoretical Revisit ALEXANDROS GEORGOGIANNIS
Bayesian optimization for automated model selection Gustavo Malkomes, Charles Schaff, Roman Garnett
A Probabilistic Model of Social Decision Making based on Reward Maximization Koosha Khalvati, Seongmin A. Park, Jean-Claude Dreher, Rajesh P. Rao
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition Ahmed M. Alaa, Mihaela van der Schaar
Fast and Flexible Monotonic Functions with Ensembles of Lattices Mahdi Milani Fard, Kevin Canini, Andrew Cotter, Jan Pfeifer, Maya Gupta
Conditional Generative Moment-Matching Networks Yong Ren, Jun Zhu, Jialian Li, Yucen Luo
Stochastic Gradient MCMC with Stale Gradients Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin
Composing graphical models with neural networks for structured representations and fast inference Matthew Johnson, David K. Duvenaud, Alex Wiltschko, Ryan P. Adams, Sandeep R. Datta
Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling Maria-Florina F. Balcan, Hongyang Zhang
Combinatorial semi-bandit with known covariance Rémy Degenne, Vianney Perchet
Matrix Completion has No Spurious Local Minimum Rong Ge, Jason D. Lee, Tengyu Ma
The Multiscale Laplacian Graph Kernel Risi Kondor, Horace Pan
Adaptive Averaging in Accelerated Descent Dynamics Walid Krichene, Alexandre Bayen, Peter L. Bartlett
Sub-sampled Newton Methods with Non-uniform Sampling Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney
Stochastic Gradient Geodesic MCMC Methods Chang Liu, Jun Zhu, Yang Song
Variational Bayes on Monte Carlo Steroids Aditya Grover, Stefano Ermon
Showing versus doing: Teaching by demonstration Mark K. Ho, Michael Littman, James MacGlashan, Fiery Cushman, Joseph L. Austerweil
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation Jianxu Chen, Lin Yang, Yizhe Zhang, Mark Alber, Danny Z. Chen
Maximization of Approximately Submodular Functions Thibaut Horel, Yaron Singer
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, Ji Liu
Learning Infinite RBMs with Frank-Wolfe Wei Ping, Qiang Liu, Alexander T. Ihler
Estimating the Size of a Large Network and its Communities from a Random Sample Lin Chen, Amin Karbasi, Forrest W. Crawford
Learning Sensor Multiplexing Design through Back-propagation Ayan Chakrabarti
On Robustness of Kernel Clustering Bowei Yan, Purnamrita Sarkar
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression Kameron D. Harris, Stefan Mihalas, Eric Shea-Brown
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild Gregory Rogez, Cordelia Schmid
New Liftable Classes for First-Order Probabilistic Inference Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization Jian Wu, Peter Frazier
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random Ilya Shpitser
Optimistic Gittins Indices Eli Gutin, Vivek Farias
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models Juho Lee, Lancelot F. James, Seungjin Choi
Launch and Iterate: Reducing Prediction Churn Mahdi Milani Fard, Quentin Cormier, Kevin Canini, Maya Gupta
“Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation Wen-Hao Zhang, He Wang, K. Y. Michael Wong, Si Wu
Learning shape correspondence with anisotropic convolutional neural networks Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael Bronstein
Pairwise Choice Markov Chains Stephen Ragain, Johan Ugander
NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang
Clustering with Same-Cluster Queries Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, koray kavukcuoglu, Geoffrey E. Hinton
Parameter Learning for Log-supermodular Distributions Tatiana Shpakova, Francis Bach
Deconvolving Feedback Loops in Recommender Systems Ayan Sinha, David F. Gleich, Karthik Ramani
Structured Matrix Recovery via the Generalized Dantzig Selector Sheng Chen, Arindam Banerjee
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making Himabindu Lakkaraju, Jure Leskovec
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks Noah Apthorpe, Alexander Riordan, Robert Aguilar, Jan Homann, Yi Gu, David Tank, H. Sebastian Seung
Designing smoothing functions for improved worst-case competitive ratio in online optimization Reza Eghbali, Maryam Fazel
Convergence guarantees for kernel-based quadrature rules in misspecified settings Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data Bo Wang, Junjie Zhu, Armin Pourshafeie, Oana Ursu, Serafim Batzoglou, Anshul Kundaje
A Non-generative Framework and Convex Relaxations for Unsupervised Learning Elad Hazan, Tengyu Ma
Equality of Opportunity in Supervised Learning Moritz Hardt, Eric Price, None, Nati Srebro
Scaled Least Squares Estimator for GLMs in Large-Scale Problems Murat A. Erdogdu, Lee H. Dicker, Mohsen Bayati
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories Yuan Zhao, Il Memming Park
Search Improves Label for Active Learning Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
Higher-Order Factorization Machines Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
Exponential expressivity in deep neural networks through transient chaos Ben Poole, Subhaneil Lahiri, Maithreyi Raghu, Jascha Sohl-Dickstein, Surya Ganguli
Split LBI: An Iterative Regularization Path with Structural Sparsity Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao
An equivalence between high dimensional Bayes optimal inference and M-estimation Madhu Advani, Surya Ganguli
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune
Deep Submodular Functions: Definitions and Learning Brian W. Dolhansky, Jeff A. Bilmes
Discriminative Gaifman Models Mathias Niepert
Leveraging Sparsity for Efficient Submodular Data Summarization Erik Lindgren, Shanshan Wu, Alexandros G. Dimakis
Local Minimax Complexity of Stochastic Convex Optimization sabyasachi chatterjee, John C. Duchi, John Lafferty, Yuancheng Zhu
Stochastic Optimization for Large-scale Optimal Transport Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis Bach
On Mixtures of Markov Chains Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii
Linear Contextual Bandits with Knapsacks Shipra Agrawal, Nikhil Devanur
Reconstructing Parameters of Spreading Models from Partial Observations Andrey Lokhov
Spatiotemporal Residual Networks for Video Action Recognition Christoph Feichtenhofer, Axel Pinz, Richard Wildes
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations Behnam Neyshabur, Yuhuai Wu, Ruslan R. Salakhutdinov, Nati Srebro
Strategic Attentive Writer for Learning Macro-Actions Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John Agapiou, koray kavukcuoglu
The Limits of Learning with Missing Data Brian Bullins, Elad Hazan, Tomer Koren
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, Walter Stewart
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani
Community Detection on Evolving Graphs Aris Anagnostopoulos, Jakub Łącki, Silvio Lattanzi, Stefano Leonardi, Mohammad Mahdian
Online and Differentially-Private Tensor Decomposition Yining Wang, Anima Anandkumar
Dimension-Free Iteration Complexity of Finite Sum Optimization Problems Yossi Arjevani, Ohad Shamir
Towards Conceptual Compression Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra
Exact Recovery of Hard Thresholding Pursuit Xiaotong Yuan, Ping Li, Tong Zhang
Data Programming: Creating Large Training Sets, Quickly Alexander J. Ratner, Christopher M. De Sa, Sen Wu, Daniel Selsam, Christopher Ré
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back Vitaly Feldman
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint Liangbei Xu, Mark Davenport
Fast Distributed Submodular Cover: Public-Private Data Summarization Baharan Mirzasoleiman, Morteza Zadimoghaddam, Amin Karbasi
Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods Cristina Savin, Gasper Tkacik
Lifelong Learning with Weighted Majority Votes Anastasia Pentina, Ruth Urner
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes Jack Rae, Jonathan J. Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Tim Lillicrap
Matching Networks for One Shot Learning Oriol Vinyals, Charles Blundell, Tim Lillicrap, koray kavukcuoglu, Daan Wierstra
Tight Complexity Bounds for Optimizing Composite Objectives Blake E. Woodworth, Nati Srebro
Graphical Time Warping for Joint Alignment of Multiple Curves Yizhi Wang, David J. Miller, Kira Poskanzer, Yue Wang, Lin Tian, Guoqiang Yu
Unsupervised Risk Estimation Using Only Conditional Independence Structure Jacob Steinhardt, Percy S. Liang
MetaGrad: Multiple Learning Rates in Online Learning Tim van Erven, Wouter M. Koolen
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Josh Tenenbaum
High Dimensional Structured Superposition Models Qilong Gu, Arindam Banerjee
Joint quantile regression in vector-valued RKHSs Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc
The Forget-me-not Process Kieran Milan, Joel Veness, James Kirkpatrick, Michael Bowling, Anna Koop, Demis Hassabis
Wasserstein Training of Restricted Boltzmann Machines Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi
Communication-Optimal Distributed Clustering Jiecao Chen, He Sun, David Woodruff, Qin Zhang
Probing the Compositionality of Intuitive Functions Eric Schulz, Josh Tenenbaum, David K. Duvenaud, Maarten Speekenbrink, Samuel J. Gershman
Ladder Variational Autoencoders Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther
The Multiple Quantile Graphical Model Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
Threshold Learning for Optimal Decision Making Nathan F. Lepora
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA Aapo Hyvarinen, Hiroshi Morioka
Can Active Memory Replace Attention? Łukasz Kaiser, Samy Bengio
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami
The Product Cut Thomas Laurent, James von Brecht, Xavier Bresson, arthur szlam
Learning Sparse Gaussian Graphical Models with Overlapping Blocks Mohammad Javad Hosseini, Su-In Lee
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar
Average-case hardness of RIP certification Tengyao Wang, Quentin Berthet, Yaniv Plan
A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control Ivan Herreros, Xerxes Arsiwalla, Paul Verschure
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst
CliqueCNN: Deep Unsupervised Exemplar Learning Miguel A. Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Bjorn Ommer
Large-Scale Price Optimization via Network Flow Shinji Ito, Ryohei Fujimaki
Online Pricing with Strategic and Patient Buyers Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar
Global Optimality of Local Search for Low Rank Matrix Recovery Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences Daniel Neil, Michael Pfeiffer, Shih-Chii Liu
Improving PAC Exploration Using the Median Of Means Jason Pazis, Ronald E. Parr, Jonathan P. How
Infinite Hidden Semi-Markov Modulated Interaction Point Process matt zhang, Peng Lin, Peng Lin, Ting Guo, Yang Wang, Yang Wang, Fang Chen
Cooperative Inverse Reinforcement Learning Dylan Hadfield-Menell, Stuart J. Russell, Pieter Abbeel, Anca Dragan
Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments Ransalu Senanayake, Lionel Ott, Simon O'Callaghan, Fabio T. Ramos
Select-and-Sample for Spike-and-Slab Sparse Coding Abdul-Saboor Sheikh, Jörg Lücke
Tractable Operations for Arithmetic Circuits of Probabilistic Models Yujia Shen, Arthur Choi, Adnan Darwiche
Greedy Feature Construction Dino Oglic, Thomas Gärtner
Mistake Bounds for Binary Matrix Completion Mark Herbster, Stephen Pasteris, Massimiliano Pontil
Data driven estimation of Laplace-Beltrami operator Frederic Chazal, Ilaria Giulini, Bertrand Michel
Tracking the Best Expert in Non-stationary Stochastic Environments Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
Learning to learn by gradient descent by gradient descent Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes Hassan A. Kingravi, Harshal R. Maske, Girish Chowdhary
Quantum Perceptron Models Ashish Kapoor, Nathan Wiebe, Krysta Svore
Guided Policy Search via Approximate Mirror Descent William H. Montgomery, Sergey Levine
The Power of Optimization from Samples Eric Balkanski, Aviad Rubinstein, Yaron Singer
Deep Exploration via Bootstrapped DQN Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization Jingwei Liang, Jalal Fadili, Gabriel Peyré
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages Yin Cheng Ng, Pawel M. Chilinski, Ricardo Silva
Convolutional Neural Fabrics Shreyas Saxena, Jakob Verbeek
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, Alejandro Ribeiro
A Sparse Interactive Model for Matrix Completion with Side Information Jin Lu, Guannan Liang, Jiangwen Sun, Jinbo Bi
Coresets for Scalable Bayesian Logistic Regression Jonathan Huggins, Trevor Campbell, Tamara Broderick
Agnostic Estimation for Misspecified Phase Retrieval Models Matey Neykov, Zhaoran Wang, Han Liu
Linear Relaxations for Finding Diverse Elements in Metric Spaces Aditya Bhaskara, Mehrdad Ghadiri, Vahab Mirrokni, Ola Svensson
Binarized Neural Networks Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
Memory-Efficient Backpropagation Through Time Audrunas Gruslys, Remi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves
Bayesian Optimization with Robust Bayesian Neural Networks Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter
Learnable Visual Markers Oleg Grinchuk, Vadim Lebedev, Victor Lempitsky
Fast Algorithms for Robust PCA via Gradient Descent Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities Michalis Titsias RC AUEB
Learning Deep Embeddings with Histogram Loss Evgeniya Ustinova, Victor Lempitsky
Spectral Learning of Dynamic Systems from Nonequilibrium Data Hao Wu, Frank Noe
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling Chengtao Li, Suvrit Sra, Stefanie Jegelka
Mapping Estimation for Discrete Optimal Transport Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes Tarun Kathuria, Amit Deshpande, Pushmeet Kohli
Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images Vladimir Golkov, Marcin J. Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox, Jens Meiler, Daniel Cremers
Linear Feature Encoding for Reinforcement Learning Zhao Song, Ronald E. Parr, Xuejun Liao, Lawrence Carin
A Minimax Approach to Supervised Learning Farzan Farnia, David Tse
Edge-exchangeable graphs and sparsity Diana Cai, Trevor Campbell, Tamara Broderick
A Locally Adaptive Normal Distribution Georgios Arvanitidis, Lars K. Hansen, Søren Hauberg
Completely random measures for modelling block-structured sparse networks Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Sparse Support Recovery with Non-smooth Loss Functions Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques
Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics Travis Monk, Cristina Savin, Jörg Lücke
Learning values across many orders of magnitude Hado P. van Hasselt, Arthur Guez, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver
Adaptive Smoothed Online Multi-Task Learning Keerthiram Murugesan, Hanxiao Liu, Jaime Carbonell, Yiming Yang
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes Matteo Turchetta, Felix Berkenkamp, Andreas Krause
Probabilistic Linear Multistep Methods Onur Teymur, Kostas Zygalakis, Ben Calderhead
Stochastic Three-Composite Convex Minimization Alp Yurtsever, Bang Cong Vu, Volkan Cevher
Using Fast Weights to Attend to the Recent Past Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu
Maximal Sparsity with Deep Networks? Bo Xin, Yizhou Wang, Wen Gao, David Wipf, Baoyuan Wang
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam T. Kalai
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data Valentina Zantedeschi, Rémi Emonet, Marc Sebban
Learning Additive Exponential Family Graphical Models via \ell_{2,1}-norm Regularized M-Estimation Xiaotong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu
Backprop KF: Learning Discriminative Deterministic State Estimators Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks Xiang Li, Tao Qin, Jian Yang, Xiaolin Hu, Tieyan Liu
Fast recovery from a union of subspaces Chinmay Hegde, Piotr Indyk, Ludwig Schmidt
Incremental Variational Sparse Gaussian Process Regression Ching-An Cheng, Byron Boots
A Consistent Regularization Approach for Structured Prediction Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi
Clustering Signed Networks with the Geometric Mean of Laplacians Pedro Mercado, Francesco Tudisco, Matthias Hein
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Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction Jacob Steinhardt, Gregory Valiant, Moses Charikar
Fast and accurate spike sorting of high-channel count probes with KiloSort Marius Pachitariu, Nicholas A. Steinmetz, Shabnam N. Kadir, Matteo Carandini, Kenneth D. Harris
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning Wouter M. Koolen, Peter Grünwald, Tim van Erven
Ancestral Causal Inference Sara Magliacane, Tom Claassen, Joris M. Mooij
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu
Tagger: Deep Unsupervised Perceptual Grouping Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, Juergen Schmidhuber
An Efficient Streaming Algorithm for the Submodular Cover Problem Ashkan Norouzi-Fard, Abbas Bazzi, Ilija Bogunovic, Marwa El Halabi, Ya-Ping Hsieh, Volkan Cevher
Interaction Networks for Learning about Objects, Relations and Physics Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, koray kavukcuoglu
Efficient state-space modularization for planning: theory, behavioral and neural signatures Daniel McNamee, Daniel M. Wolpert, Mate Lengyel
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent Chi Jin, Sham M. Kakade, Praneeth Netrapalli
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics Wei-Shou Hsu, Pascal Poupart
Computing and maximizing influence in linear threshold and triggering models Justin T. Khim, Varun Jog, Po-Ling Loh
Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions Yichen Wang, Nan Du, Rakshit Trivedi, Le Song
Optimal Learning for Multi-pass Stochastic Gradient Methods Junhong Lin, Lorenzo Rosasco
Generative Adversarial Imitation Learning Jonathan Ho, Stefano Ermon
Latent Attention For If-Then Program Synthesis Chang Liu, Xinyun Chen, Eui Chul Shin, Mingcheng Chen, Dawn Song
Dual Space Gradient Descent for Online Learning Trung Le, Tu Nguyen, Vu Nguyen, Dinh Phung
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds Hongyi Zhang, Sashank J. Reddi, Suvrit Sra
Professor Forcing: A New Algorithm for Training Recurrent Networks Alex M. Lamb, Anirudh Goyal ALIAS PARTH GOYAL, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio
Learning brain regions via large-scale online structured sparse dictionary learning Elvis DOHMATOB, Arthur Mensch, Gael Varoquaux, Bertrand Thirion
Efficient Neural Codes under Metabolic Constraints Zhuo Wang, Xue-Xin Wei, Alan A. Stocker, Daniel D. Lee
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods Andrej Risteski, Yuanzhi Li
Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information Alexander Shishkin, Anastasia Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov
Bayesian Intermittent Demand Forecasting for Large Inventories Matthias W. Seeger, David Salinas, Valentin Flunkert
Visual Question Answering with Question Representation Update (QRU) Ruiyu Li, Jiaya Jia
Learning Parametric Sparse Models for Image Super-Resolution Yongbo Li, Weisheng Dong, Xuemei Xie, GUANGMING Shi, Xin Li, Donglai Xu
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning Jean-Bastien Grill, Michal Valko, Remi Munos
Asynchronous Parallel Greedy Coordinate Descent Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, James Demmel, Cho-Jui Hsieh
Iterative Refinement of the Approximate Posterior for Directed Belief Networks Devon Hjelm, Ruslan R. Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince Calhoun, Junyoung Chung
Assortment Optimization Under the Mallows model Antoine Desir, Vineet Goyal, Srikanth Jagabathula, Danny Segev
Disease Trajectory Maps Peter Schulam, Raman Arora
Multistage Campaigning in Social Networks Mehrdad Farajtabar, Xiaojing Ye, Sahar Harati, Le Song, Hongyuan Zha
Learning in Games: Robustness of Fast Convergence Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Eva Tardos
Improved Variational Inference with Inverse Autoregressive Flow Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
Algorithms and matching lower bounds for approximately-convex optimization Andrej Risteski, Yuanzhi Li
Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization Tyler B. Johnson, Carlos Guestrin
Kernel Bayesian Inference with Posterior Regularization Yang Song, Jun Zhu, Yong Ren
Neural Universal Discrete Denoiser Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon
Optimal Architectures in a Solvable Model of Deep Networks Jonathan Kadmon, Haim Sompolinsky
Conditional Image Generation with PixelCNN Decoders Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, koray kavukcuoglu, Oriol Vinyals, Alex Graves
Supervised Learning with Tensor Networks Edwin Stoudenmire, David J. Schwab
Multi-step learning and underlying structure in statistical models Maia Fraser
Structure-Blind Signal Recovery Dmitry Ostrovsky, Zaid Harchaoui, Anatoli Juditsky, Arkadi S. Nemirovski
An Architecture for Deep, Hierarchical Generative Models Philip Bachman
Feature selection in functional data classification with recursive maxima hunting José L. Torrecilla, Alberto Suárez
Achieving budget-optimality with adaptive schemes in crowdsourcing Ashish Khetan, Sewoong Oh
Near-Optimal Smoothing of Structured Conditional Probability Matrices Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky
Supervised Word Mover's Distance Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger
Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel
Full-Capacity Unitary Recurrent Neural Networks Scott Wisdom, Thomas Powers, John Hershey, Jonathan Le Roux, Les Atlas
Threshold Bandits, With and Without Censored Feedback Jacob D. Abernethy, Kareem Amin, Ruihao Zhu
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks Wenjie Luo, Yujia Li, Raquel Urtasun, Richard Zemel
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods Lev Bogolubsky, Pavel Dvurechensky, Alexander Gasnikov, Gleb Gusev, Yurii Nesterov, Andrei M. Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii
k*-Nearest Neighbors: From Global to Local Oren Anava, Kfir Levy
Normalized Spectral Map Synchronization Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi
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A posteriori error bounds for joint matrix decomposition problems Nicolo Colombo, Nikos Vlassis
A Bayesian method for reducing bias in neural representational similarity analysis Ming Bo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes Chris Junchi Li, Zhaoran Wang, Han Liu
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvari
SDP Relaxation with Randomized Rounding for Energy Disaggregation Kiarash Shaloudegi, András György, Csaba Szepesvari, Wilsun Xu
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates Yuanzhi Li, Yingyu Liang, Andrej Risteski
Unsupervised Learning of 3D Structure from Images Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess
Poisson-Gamma dynamical systems Aaron Schein, Hanna Wallach, Mingyuan Zhou
Gaussian Processes for Survival Analysis Tamara Fernandez, Nicolas Rivera, Yee Whye Teh
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep K. Ravikumar, Inderjit S. Dhillon
Optimal Binary Classifier Aggregation for General Losses Akshay Balsubramani, Yoav S. Freund
Disentangling factors of variation in deep representation using adversarial training Michael F. Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, Yann LeCun
A primal-dual method for conic constrained distributed optimization problems Necdet Serhat Aybat, Erfan Yazdandoost Hamedani
Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing Farshad Lahouti, Babak Hassibi
An Online Sequence-to-Sequence Model Using Partial Conditioning Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio
Learning to Poke by Poking: Experiential Learning of Intuitive Physics Pulkit Agrawal, Ashvin V. Nair, Pieter Abbeel, Jitendra Malik, Sergey Levine
Learning Deep Parsimonious Representations Renjie Liao, Alex Schwing, Richard Zemel, Raquel Urtasun
Only H is left: Near-tight Episodic PAC RL

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