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