Tong Zhang's research papers(文献资料)

Tong Zhang's research papers

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Tech_Reports:

[TR] Shai Shalev-Shwartz and Tong Zhang. Proximal Stochastic Dual Coordinate Ascent, Tech Report arXiv:1211.2717, Nov 2012. [Software: C++ code for solving L1-L2 Regularization]

[TR] Xiaotong Yuan and Tong Zhang.Partial Gaussian Graphical Model Estimation, Tech Report arXiv:1209.6419, Sep 2012.

[TR] Shai Shalev-Shwartz and Tong Zhang. Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization, Tech Report arXiv:1209.1873, Sep 2012.

[TR] Lin Xiao and Tong Zhang.A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem, Tech Report arXiv:1203.3002, March 2012.

[TR] Cun-hui Zhang and Tong Zhang.A General Framework of Dual Certificate Analysis for Structured Sparse Recovery Problems, Tech Report arXiv:1201.3302, Jan 2012.

[TR] Xiaotong Yuan and Tong Zhang.Truncated Power Method for Sparse Eigenvalue Problems, Tech Report arXiv:1112.2679, Dec 2011.

[TR] Rie Johnson and Tong Zhang.Learning Nonlinear Functions Using Regularized Greedy Forest, Tech Report arXiv:1109.0887, Sept 2011.

[TR] Animashree Anandkumar and Kamalika Chaudhuri and Daniel Hsu and Sham M. Kakade and Le Song and Tong Zhang.Spectral Methods for Learning Multivariate Latent Tree Structure, Tech Report arXiv:1107.1283, July 2011.

[TR] Daniel Hsu and Sham M. Kakade and Tong Zhang. An Analysis of Random Design Linear Regression, Tech Report arXiv:1106.2363, June 2011.

[TR] Alina Beygelzimer and Daniel Hsu and John Langford and Tong Zhang. Agnostic Active Learning without Constraints, Tech Report arXiv:1006.2588, June 2010.

[TR] Dean P. Foster, Rie Johnson, Sham M. Kakade and Tong Zhang. Multi-View Dimensionality Reduction via Canonical Correlation Analysis, May Tech Report, 2009.

2012:

[114] Dong Dai and Philippe Rigollet and Tong Zhang. Deviation Optimal Learning using Greedy Q-aggregation,Annals of Statistics, to appear.

[113] Zhipeng Cai, Mariette F Ducatez, Jialiang Yang, Tong Zhang, Li-Ping Long, Adrianus C. Boon, Richard J. Webby and Xiu-Feng Wan.Identifying antigenicity associated sites in highly pathogenic H5N1 influenza virus hemagglutinin by using sparse learning.Journal of Molecular Biology, 2012.

[112] Tong Zhang.Multistage Convex Relaxation for Feature Selection,Bernoulli, 2012.

[111] Daniel Hsu and Sham M. Kakade and Tong Zhang. A Spectral Algorithm for Learning Hidden Markov Models, Journal of Computer and System Sciences, 2012.

[110] Cun-hui Zhang and Tong Zhang.A General Theory of Concave Regularization for High Dimensional Sparse Estimation Problems,Statistical Science, 2012.

[109] Daniel Hsu and Sham M. Kakade and Tong Zhang. A tail inequality for quadratic forms of sub-Gaussian random vectors,Electronic Communications in Probability, 52, article 14, 2012.

[108] Daniel Hsu and Sham M. Kakade and Tong Zhang. Tail inequalities for sums of random matrices that depend on the intrinsic dimension,Electronic Communications in Probability, 17, article 14, 2012.

[107] Quanquan Gu and Tong Zhang and Chris Ding and Jiawei Han. Selective Labeling via Error Bound Minimization. NIPS 12, 2012.

[106] Daniel Hsu and Sham M. Kakade and Tong Zhang . Random Design Analysis of Ridge Regression, COLT 12, 2012. [full version]

[105] Lin Xiao and Tong Zhang. A Proximal-Gradient Homotopy Method for the L1-Regularized Least-Squares Problem,ICML 12, 2012. [full version]

2011:

[104] Junzhou Huang, Tong Zhang and Dimitris Metaxas. Learning with Structured Sparsity,JMLR, 12:3371-3412, 2011.

[103] Wenyuan Li and Chun-Chi Liu and Tong Zhang and Haifeng Li and Michael S. Waterman and Xianghong Jasmine Zhou.Integrative Analysis of Many Weighted Co-expression Networks Using Tensor Computation,PLoS Comput. Biol 7(6) e1001106, (url) 2011.

[102] Daniel Hsu and Sham Kakade and Tong Zhang. Robust Matrix Decomposition with Sparse Corruptions,IEEE Trans. Info. Th, 57:7221-7234, 2011.

[101] Tong Zhang.Sparse Recovery with Orthogonal Matching Pursuit under RIP,IEEE Trans. Info. Th, 57:5215-6221, 2011.

[100] Tong Zhang.Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations,IEEE Trans. Info. Th, 57:4689-4708, 2011. (software: R source package)

[99] Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong. Learning to Search Efficiently in High Dimensions, NIPS 11, 2011.

[98] Animashree Anandkumar, Kamalika Chaudhuri, Daniel Hsu, Sham M. Kakade, Le Song, and Tong Zhang. Spectral methods for learning multivariate latent tree structure, NIPS 11, 2011.

[97] Dong Dai and Tong Zhang. Greedy Model Averaging, NIPS�€™11, 2010. [improved version]

[96] Miroslav Dudik and Daniel Hsu and Satyen Kale and Nikos Karampatziakis and John Langford and Lev Reyzin and Tong Zhang.Efficient Optimal Learning for Contextual Bandits,UAI 2011. (arxiv 1106.2369)

2010:

[95] Zhipeng Cai and Tong Zhang and Xiu-Feng Wan. A Computational Framework for Influenza Antigenic Cartography.PLoS Comput Biol, 6(10) e1000949 (url), 2010.

[94] Shai Shalev-Shwartz and Nathan Srebro and Tong Zhang. Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints,Siam Journal on Optimization, 20:2807-2832, 2010.

[93] Junzhou Huang and Tong Zhang.The Benefit of Group Sparsity.Annals of Statistics, 38:1978-2004, 2010.

[92] Tong Zhang.Analysis of Multi-stage Convex Relaxation for Sparse Regularization,Journal of Machine Learning Research, 11:1081-1107, 2010.

[91] Tong Zhang.Fundamental Statistical Techniques, Chapter in Handbook of Natural Language Processing, Chapman & Hall/CRC, 2010.

[90] Alina Beygelzimer and Daniel Hsu and John Langford and Tong Zhang. Agnostic Active Learning Without Constraints, NIPS 10, 2010.

[89] Yuanqing Lin and Tong Zhang and Shenghuo Zhu and Kai Yu. Deep Coding Networks. NIPS 10, 2010.

[88] Xi Zhou and Kai Yu and Tong Zhang and Thomas Huang. Image Classification using Super-Vector Coding of Local Image Descriptors, ECCV 10, 2010.

[87] Kai Yu and Tong Zhang.Improved Local Coordinate Coding using Local Tangents, In ICML 10, 2010.

2009:

[86] Tong Zhang.Some Sharp Performance Bounds for Least Squares Regression with L1 Regularization.Annals of Statistics, 37:2109-2114, 2009.

[85] Andrei Broder, Marcus Fontoura, Evgeniy Gabrilovich, Amruta Joshi, Vanja Josifovski, Lance Riedel and Tong Zhang.Classifying Search Quries Using the Web as a Source of Knowledge.ACM Transactions on the Web,3:1-28, 2009.

[84] John Langford, Lihong Li and Tong Zhang. Sparse Online Learning via Truncated Gradient.Journal of Machine Learning Research, 10:777-801, 2009.

[83] Tong Zhang.On the Consistency of Feature Selection using Greedy Least Squares Regression. Journal of Machine Learning Research, 10:555-568, 2009.

[82] Junzhou Huang, Tong Zhang and Dimitris Metaxas. Learning with Structured Sparsity. In ICML 09, 2009.

[81] John Langford, Ruslan Salakhutdinov and Tong Zhang. Learning Nonlinear Dynamic Models. In ICML 09, 2009.

[80] Daniel Hsu and Sham M. Kakade and Tong Zhang. A Spectral Algorithm for Learning Hidden Markov Models, In COLT 09, 2009.

[79] Kai Yu and Tong Zhang and Yihong Gong. Nonlinear Learning using Local Coordinate Coding, In NIPS 09, 2009. (full version)

[78] Daniel Hsu and Sham M. Kakade and John Langford and Tong Zhang. Multi-label Prediction via Compressed Sensing, In NIPS 09, 2009.

2008:

[77] David Cossock and Tong Zhang.Statistical Analysis of Bayes Optimal Subset Ranking.IEEE Trans. Info. Theory, 54:4140-5154, 2008.

[76] Christoph Tillmann and Tong Zhang. An Online Relevant Set Algorithm for Statistical Machine Translation.IEEE Transactions on Audio, Speech, and Language processing, 16: 1274-1286, 2008.

[75] Rie Johnson and Tong Zhang.Graph-based semi-supervised learning and spectral kernel design.IEEE Trans. Info. Theory, 54:275-288, 2008.

[74] Tong Zhang.Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models. In NIPS 08, 2008. (full version)

[73] Tong Zhang.Multi-stage Convex Relaxation for Learning with Sparse Regularization. In NIPS 08, 2008. (software: R source package)

[72] John Langford, Lihong Li and Tong Zhang. Sparse Online Learning via Truncated Gradient. In NIPS'08, 2008.

2007:

[71] Rie Johnson and Tong Zhang.On the effectiveness of Laplacian normalization for graph semi-supervised learning. JMLR, 8:1489-1517, 2007.

[70] Christoph Tillmann and Tong Zhang. A block bigram prediction model for statistical machine translation.ACM Transactions on Speech and Language Processing , 4, 2007.

[69] Maria-Florina Balcan, Andrei Broder, and Tong Zhang. Margin based active learning. In COLT'07, 2007.

[68] Rie K. Ando and Tong Zhang.Two-view feature generation model for semi-supervised learning. In ICML'07, 2007.

[67] John Langford and Tong Zhang.The Epoch-Greedy algorithm for multiarmed bandits with side information. In NIPS'07, 2007.

[66] Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle Keke Chen, Gordon Sun. A general boosting method and its application to learning ranking functions for web search. In NIPS'07, 2007.

[65] Andrei Broder, Marcus Fontoura, Evgeniy Gabrilovich, Amruta Joshi,Vanja Josifovski, and Tong Zhang. Robust classification of rare queries using web knowledge. In SIGIR'07, 2007.

2006:

[64] Tong Zhang.Information Theoretical Upper and Lower Bounds for Statistical Estimation.IEEE Transaction on Information Theory, 52:1307-1321, 2006.

[63] Tong Zhang.From epsilon-entropy to KL-entropy: analysis of minimum information complexity density estimation.The Annals of Statistics, 34:2180-2210, 2006.

[62] Christoph Tillmann and Tong Zhang. A discriminative global training algorithm for statistical MT. In ACL'06, 2006 (full version is [76]).

[61] Tong Zhang, Alexandrin Popescul, and Byron Dom. Linear prediction models with graph regularization for web-page categorization. In KDD'06, 2006.

[60] Rie K. Ando and Tong Zhang. Learning on graph with Laplacian regularization. In NIPS, 2006 (full paper).

[59] David Cossock and Tong Zhang. Subset ranking using regression. In Proc. COLT'06, 2006 (long version is [77] ).

[58] Rie K. Ando, Mark Dredze and Tong Zhang. TREC 2005 Genomics Track Experiments at IBM Watson.Proceedings of TREC 05, 2006.

2005:

[57]Rie K. Ando and Tong Zhang.A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data.JMLR, 6:1817-1853, 2005.

[56]Tong Zhang and Bin Yu.Boosting with early stopping: Convergence and Consistency.The Annals of Statistics,33:1538-1579, 2005.

[55]Tong Zhang.Learning Bounds for Kernel Regression using Effective Data Dimensionality.Neural Computation, 17:2077-2098, 2005.

[54]Tong Zhang and Rie K. Ando. Analysis of Spectral Kernel Design based Semi-supervised Learning. NIPS, 2005 (long version is[75]).

[53]Christoph Tillmann and Tong Zhang.A Localized Prediction Model for Statistical Machine Translation. ACL 05.

[52] Rie Ando and Tong Zhang.A High-Performance Semi-Supervised Learning Method for Text Chunking. ACL 05 (also see [57]).

[51] Tong Zhang.Localized Upper and Lower Bounds for Some Estimation Problems. COLT 2005.

[50]Tong Zhang.Data Dependent Concentration Bounds for Sequential Prediction Algorithms. COLT 2005.

2004:

[49]Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, and Fred Damerau. Text Mining: Predictive Methods for Analyzing Unstructured Information, Springer-Verlag, New York, 2004.

[48]Tong Zhang.Statistical Analysis of Some Multi-Category Large Margin Classification Methods.JMLR, 5:1225-1251, 2004.

[47]Fred J. Damerau, Tong Zhang, Sholom M. Weiss, and Nitin Indurkhya.Text categorization for a comprehensive time-dependent benchmark.Information Processing & Management, 40:209-221, 2004.

[46]Tong Zhang.Statistical behavior and consistency of classification methods based on convex risk minimization.The Annals of Statistics, 32:56-85, 2004 (with discussion).

[45] Tong Zhang.Class-size independent generalization analsysis of some discriminative multi-category classification methods. NIPS, 2004.

[44]Jinbo Bi and Tong Zhang.Support vector classification with input data uncertainty. NIPS, 2004.

[43]Tong Zhang.Solving Large Scale Linear Prediction Problems Using Stochastic Gradient Descent Algorithms. ICML, 2004.

[42] Li Zhang, Yue Pan, and Tong Zhang. Focused Named Entity Recognition using Machine Learning. SIGIR, 2004.

[41] Tong Zhang.On the Convergence of MDL Density Estimation. COLT, 2004.

[40] Jinbo Bi, Tong Zhang, and Kristin P. Bennett. Column-Generation Boosting Methods for Mixture of Kernels. KDD, 2004.

2003:

[39]Ron Meir and Tong Zhang.Generalization error bounds for Bayesian mixture algorithms.Journal of Machine Learning Research, 4:839-860, 2003.

[38]Shie Mannor, Ron Meir, and Tong Zhang.Greedy algorithms for classification - consistency, convergence rates, and adaptivity.Journal of Machine Learning Research, 4:713-741, 2003.

[37]Tong Zhang.Sequential greedy approximation for certain convex optimization problems.IEEE Transaction on Information Theory, 49:682-691, 2003.

[36]Tong Zhang.Leave-one-out bounds for kernel methods.Neural Computation, 15:1397-1437, 2003.

[35]Sholom M. Weiss and Tong Zhang. The Handbook of Data Mining, Chapter on Performance Analysis and Evaluation.Lawrence Erlbaum Associates, 2003.

[34]Tong Zhang.An infinity-sample theory for multi-category large margin classification. In NIPS 03, 2004. to appear.

[33]Tong Zhang.Learning bounds for a generalized family of Bayesian posterior distributions. In NIPS 03, 2004. to appear. (also see [59])

[32]Tong Zhang and Bin Yu. On the convergence of boosting procedures. InICML 03, pages 904-911, 2003. (full paper)

[31]Radu Florian, Abe Ittycheriah, Hongyan Jing, and Tong Zhang.Named entity recogintion through classifier combination. In Proceedings CoNLL 03, pages 168-171, 2003.

[30]Tong Zhang and David E. Johnson.A robust risk minimization based named entity recognition system. In Proceedings CoNLL 03, pages 204-207, 2003.

[29]Tong Zhang, Fred Damerau, and David E. Johnson.Updating an NLP system to fit new domains: an empirical study on the sentence segmentation problem. In ProceedingsCoNLL 03, pages 56-62, 2003.

[28]Hongyan Jing, Radu Florian, Xiaoqiang Luo, Tong Zhang, and Abraham Ittycheriah.Howtogetachinesename (entity) : Segmentation and combination issues. In EMNLP 03, 2003.

2002:

[27]David E. Johnson, Frank J. Oles, Tong Zhang, and Thilo Goetz.A decision-tree-based symbolic rule induction system for text categorization.IBM Systems Journal, 41:428-437, 2002.

[26]Tong Zhang and Carlo Tomasi.On the consistency of instantaneous rigid motion estimation.International Journal of Computer Vision, 46:51-79, 2002.

[25]Tong Zhang.Covering number bounds of certain regularized linear function classes.Journal of Machine Learning Research, 2:527-550, 2002.

[24]Tong Zhang and Vijay S. Iyengar.Recommender systems using linear classifiers.Journal of Machine Learning Research, 2:313-334, 2002.

[23]Tong Zhang, Fred Damerau, and David E. Johnson.Text chunking based on a generalization of Winnow.Journal of Machine Learning Research, 2:615-637, 2002.

[22]Tong Zhang.On the dual formulation of regularized linear systems.Machine Learning, 46:91-129, 2002.

[21]Tong Zhang.Approximation bounds for some sparse kernel regression algorithms.Neural Computation, 14:3013-3042, 2002.

[20]Jane Cullum and Tong Zhang.Two-sided Arnoldi and non-symmetric Lanczos algorithms.SIAM Journal on Matrix Analysis and Applications, 24:303-319, 2002.

[19]Ron Meir and Tong Zhang. Data-dependent bounds for Bayesian mixture methods. InNIPS 02, 2003. (full paper [39])

[18]Tong Zhang. Effective dimension and generalization of kernel learning. InNIPS 02, 2003. (full paper)

[17]Shie Mannor, Ron Meir, and Tong Zhang.The consistency of greedy algorithms for classification. In COLT 02, pages 319-333, 2002. (also see [38])

[16]Tong Zhang. Statistical behavior and consistency of support vector machines, boosting, and beyond. InICML 02, pages 690-697, 2002. (full paper [44])

[15] Fred J. Damerau, Tong Zhang, Sholom M. Weiss, and Nitin Indurkhya. Experiments in high-dimensional text categorization. InSIGIR 2002, 2002. (full paper [45])

2001:

[14]Tong Zhang and Frank J. Oles.Text categorization based on regularized linear classification methods.Information Retrieval, 4:5-31, 2001.

[13]Tong Zhang and Gene H. Golub.Rank-one approximation to high order tensors.SIAM Journal on Matrix Analysis and Applications, 23:534-550, 2001.

[12]Tong Zhang.A general greedy approximation algorithm with applications. In NIPS 01, 2002. (Also see [37])

[11]Tong Zhang.Generalization performance of some learning problems in Hilbert functional spaces. InNIPS 01, 2002.

[10] Vajay S. Iyengar and Tong Zhang. Empirical study of recommender systems using linear classifiers. InThe Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 16-27, 2001. (full paper [24])

[9] Tong Zhang. Some sparse approximation bounds for regression problems. In ICML 01, pages 624-631, 2001. (full paper [21])

[8] Tong Zhang, Fred Damerau, and David E. Johnson. Text chunking using regularized Winnow. In ACL 01, pages 539-546, 2001. (full paper [23])

[7] Tong Zhang.A sequential approximation bound for some sample-dependent convex optimization problems with applications in learning. InCOLT 01, pages 65-81, 2001.

[6] Tong Zhang. A leave-one-out cross validation bound for kernel methods with applications in learning. InCOLT 01, pages 427-443, 2001. (full paper [36])

2000:

[5]Jane Cullum, Albert Ruehli, and Tong Zhang.A method for reduced-order modeling and simulation of large interconnect circuits and its application to PEEC models including retardation.IEEE Trans. Circ. Sys., 47:261-273, 2000.

[4] Tong Zhang.Convergence of large margin separable linear classification. In NIPS 00, pages 357-363, 2001.

[3] Tong Zhang.Regularized Winnow methods. In NIPS 00, pages 703-709, 2001. (note: A typo in Thm 3.2 of the original paper is fixed)

[2] Tong Zhang and Frank J. Oles. A probability analysis on the value of unlabeled data for classification problems. InICML 00, pages 1191-1198, 2000. (note: we didn't write a longer version of the paper, in spite of comments in the paper suggesting so)

[1] Vijay S. Iyengar, Chid Apte, and Tong Zhang. Active learning using adaptive resampling. InThe Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 91-98, 2000.

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Some earlier papers:

T. Zhang, G. Golub, and K.H. Law. Subspace iterative methods for eigenvalue problems.Lin. Alg. and Appl., 294:239-258, 1999.

T. Zhang. Some theoretical results concerning the convergence of composition of regularized linear functions. InNIPS 99, pages 370-376, 2000.

T. Zhang and C. Tomasi. Fast, robust, and consistent camera motion estimation. InCVPR 99, pages 164-170, 1999.

T. Zhang. Theoretical analysis of a class of randomized regularization methods. InCOLT 99, pages 156-163, 1999.

T. Zhang, K.H. Law, and G. Golub. On the homotopy method for perturbed symmetric generalized eigenvalue problems.SIAM J. Sci. Comput., 19:1625-1645, 1998.

T. Zhang, G. Golub, and K.H. Law. Eigenvalue perturbation and the generalized Krylov subspace method.J. Applied Numer. Math., 27:185-202, 1998.

T. Zhang. Compression by model combination. In Proceedings of IEEE Data Compression Conference, DCC'98, pages 319-328, 1998.

J. Cullum, A. Ruehli, and T. Zhang. Model reduction for peec models including retardation. InProc. IEEE 7th topical meeting on Electrical performance of electronic packaging, EPEP'98, pages 287-290, 1998.

D. Greene, F. Yao, and T. Zhang. A linear algorithm for optimal context clustering with application to bi-level image coding. InIEEE Conference on image processing, ICIP'98, pages 508-511, 1998.

D. Greene, M. Vishwanath, F. Yao, and T. Zhang. A progressive Ziv-Lempel algorithm for image compression. InProceedings of Compression and Complexity of Sequences, SEQUENCE'97, pages 136-144, 1997.

G. Taubin, T. Zhang, and G. Golub. Optimal surface smoothing as filter design. InProceedings of Fourth European Conference on Computer Vision, pages 283-292, 1996.

R.S. Strichartz, A. Taylor, and T. Zhang. Densities of self-similar measures on the line.Exper. Math., 4:101-128, 1995.