Tech_Reports

[TR] Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang. Efficient Distributed Learning with Sparsity, Tech Report arXiv:1605.07991, May 2016.

[TR] Tan, Kean Ming; Wang, Zhaoran; Liu, Han; Zhang, Tong. Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow, Tech Report arXiv:1604.08697, April 2016.

[TR] Lei Han, Ting Yang, Tong Zhang. Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression, Tech Report arXiv:1604.08098, April 2016.

[TR] Shun Zheng, Fen Xia, Wei Xu, Tong Zhang. A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization, Tech Report arXiv:1604.03763, April 2016.

[TR] Chris J. Li, Mengdi Wang, Han Liu, Tong Zhang. Near-Optimal Stochastic Approximation for Online Principal Component Estimation, Tech Report arXiv:1603.05305, March 2016.

[TR] Shusen Wang and Zhihua Zhang and Tong Zhang. Improved Analyses of the Randomized Power Method and Block Lanczos Method, Tech Report arXiv:1508.06429, Aug 2015.

[TR] Da Tang and Tong Zhang. On the Duality Gap Convergence of ADMM Methods, Tech Report  arXiv:1508.03702, Aug 2015.

[TR] Jianqing Fan and Han Liu and Qiang Sun and Tong Zhang. TAC for Sparse Learning: Simultaneous Control of Algorithmic Complexity and Statistical Error, Tech Report  arXiv:1507.01037, July 2015.

[TR] Tuo Zhao and Han Liu and Tong Zhang. A General Theory of Pathwise Coordinate Optimization, Tech Report arXiv:1412.7477, Dec 2014.

[TR] Dong Dai and Tong Zhang. Bayesian Model Averaging with Exponentiated Least Square Loss, Tech Report  arXiv:1408.1234, August 2014.

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

2016:  

[150] Shusen Wang, Zhihua Zhang, and Tong Zhang. Towards more efficient SPSD matrix approximation and CUR matrix decomposition. JMLR, 17:1-49, 2016.

[149] Shai Shalev-Shwartz and Tong Zhang. Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization, Mathematical Programming, 155:105-145, 2016. [Also see arXiv:1211.2717].

[148] Xiaotong Yuan and Ping Li and Tong Zhang. Exact Recovery of Hard Thresholding Pursuit , NIPS 2016.

[147] Xiaotong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu. Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation, NIPS 2016.

[146] Rie Johnson and Tong Zhang. Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings, ICML 2016.

[145] Zhuoran Yang and Zhaoran Wang and Han Liu and Yonina C. Eldar and Tong Zhang. Sparse Nonlinear Regression: Parameter Estimation under Nonconvexit, ICML 2016.

[144] Lei Han and Yu Zhang and Xiu-Feng Wan and Tong Zhang. Generalized Hierarchical Sparse Model for Arbitrary-Order Interactive Antigenic Sites Identification in Flu Virus Data, KDD 2016

[143] Lei Han and Yu Zhang and Tong Zhang. Fast Component Pursuit for Large-Scale Inverse Covariance Estimation, KDD 2016.

2015:  

[142] Sivan Sabato and Shai Shalev-Shwartz and Nathan Srebro and Daniel Hsu and Tong Zhang. Learning Sparse Low-Threshold Linear Classifiers, JMLR 16:1275-1304, 2015.

[141] Daniel Vainsencher and Han Liu and Tong Zhang. Local Smoothness in Variance Reduced Optimization, NIPS, 2015.

[140] Zheng Qu and Peter Richtárik and Tong Zhang. Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling. NIPS, 2015. [full version]

[139] Rie Johnson and Tong Zhang. Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding. NIPS, 2015. [code]

[138] Peilin Zhao and Jinwei Yang and Tong Zhang and Ping Li. Adaptive Stochastic Alternating Direction Method of Multipliers. ICML, 2015.

[137] Peilin Zhao and Tong Zhang. Stochastic Optimization with Importance Sampling for Regularized Loss Minimization, ICML, 2015. [full version]

[136] Rie Johnson and Tong Zhang. Effective Use of Word Order for Text Categorization with Convolutional Neural Networks, NAACL, 2015.

[135] Tian Tian and Jun Zhu and Fen Xia and Xin Zhuang and Tong Zhang. Crowd Fraud Detection in Internet Advertising, WWW, 2015.

2014:  

[134] Lin Xiao and Tong Zhang. A Proximal Stochastic Gradient Method with Progressive Variance Reduction, Siam Journal on Optimization,  24:2057-2075, 2014.

[133] Zhaoran Wang and Han Liu and Tong Zhang. Optimal Computational and Statistical Rates of Convergence for Sparse Nonconvex Learning Problems, Annals of Statistics, 42:2164-2201, 2014.

[132] Xiaotong Yuan and Tong Zhang. Partial Gaussian Graphical Model Estimation, IEEE Trans. Info. Th., 60:1673-1687, 2014.

[131] Daniel Hsu and Sham M. Kakade and Tong Zhang. Random Design Analysis of Ridge Regression, Foundations of Computational Mathematics, March 2014.

[130] Rie Johnson and Tong Zhang. Learning Nonlinear Functions Using Regularized Greedy Forest, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(5):942-954, May 2014.

[129] Dong Dai and Philippe Rigollet and Lucy Xia and Tong Zhang. Aggregation of Affine Estimators, Electron. J. Statist, 8:302--327, 2014.

[128] Quanquan Gu and Tong Zhang and Jiawei Han. Batch-Mode Active Learning via Error Bound Minimization, UAI 2014.

[127] Ping Li and Cun-Hui Zhang and Tong Zhang. Compressed Counting Meets Compressed Sensing, COLT 2014. [full paper]

[126] Shai Shalev-Shwartz and Tong Zhang. Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization, ICML 2014. [full version

[125] Ohad Shamir and Nathan Srebro and Tong Zhang. Communication-Efficient Distributed Optimization using an Approximate Newton-type Method, ICML 2014. [full version]

[124] Peng Sun and Tong Zhang and Jie Zhou. A Convergence Rate Analysis for LogitBoost, MART and Their Variant, ICML 2014.

[123] Xiaotong Yuan and Ping Li and Tong Zhang. Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization, ICML 2014 [full version].

2013:  

[122] Lin Xiao and Tong Zhang. A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem, SIAM Journal on Optimization, 23:1062--1091, 2013.

[121] Xiaotong Yuan and Tong Zhang. Truncated Power Method for Sparse Eigenvalue Problems, JMLR 14:899-925, 2013.

[120] Shai Shalev-Shwartz and Tong Zhang. Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization, JMLR 14:567-599, 2013.

[119]Tong Zhang. Multistage Convex Relaxation for Feature Selection, Bernoulli, 19:2277-2293, 2013.

[118] Xiao-Tong Yuan and Tong Zhang and Xiu-Feng Wan. A joint matrix completion and filtering model for influenza serological data integration, 8(7):e69842, PLoS One, 2013.

[117] Shai Shalev-Shwartz and Tong Zhang. Accelerated Mini-Batch Stochastic Dual Coordinate Ascent, NIPS 2013.

[116] Rie Johnson and Tong Zhang. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction, NIPS 2013.

[115]Krishnakumar Balasubramanian and Kai Yu and Tong Zhang. High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning, UAI 2013.

[114] Ohad Shamir and Tong Zhang. Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes, ICML 2013.

2012:  

[113] Dong Dai and Philippe Rigollet and Tong Zhang. Deviation Optimal Learning using Greedy Q-aggregation, Annals of Statistics, 40:1878-1905, 2012.

[112] 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.

[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. [full version]

[97] Dong Dai and Tong Zhang. Greedy Model Averaging, NIPS̢۪11, 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. [full version]

[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 algorithmsJournal of Machine Learning Research, 4:839-860, 2003.

[38] Shie Mannor, Ron Meir, and Tong Zhang. Greedy algorithms for classification - consistency, convergence rates, and adaptivityJournal 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. In ICML 03, pages 904-911, 2003.  (full paper)

[31] Radu  Florian,  Abe  IttycheriahHongyan  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 Proceedings CoNLL 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 TomasiOn the consistency of instantaneous rigid motion estimationInternational Journal of Computer Vision, 46:51-79, 2002.

[25] Tong Zhang.  Covering number bounds of certain regularized linear function classesJournal 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 WinnowJournal 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 algorithmsSIAM Journal on Matrix Analysis and Applications, 24:303-319, 2002.

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

[18] Tong Zhang. Effective dimension and generalization of kernel learning. In NIPS 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. In ICML 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. In SIGIR 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. In NIPS 01, 2002.

[10] Vajay S. Iyengar and Tong Zhang. Empirical study of recommender systems using linear classifiers. In The 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. In  COLT 01, pages 65-81, 2001.

[6] Tong Zhang. A leave-one-out cross validation bound for kernel methods with applications in learning. In COLT 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.  In ICML 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. In The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 91-98, 2000.

 


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. In NIPS 99, pages 370-376, 2000.

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

T. Zhang. Theoretical analysis of a class of randomized regularization methods. In COLT 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. In Proc. 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. In IEEE 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. In Proceedings 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.  In Proceedings 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.