|
[TR] Cong Fang, Zhouchen Lin, Tong Zhang. Sharp Analysis for Nonconvex SGD Escaping from Saddle Points, Tech Report arXiv:1902.00247, Feb 2019. [TR] Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang. Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack, Tech Report arXiv:1812.11377, Dec 2018. [TR] Shixiang Chen, Shiqian Ma, Anthony Man-Cho So, Tong Zhang. Proximal Gradient Method for Manifold Optimization , Tech Report arXiv:1811.00980, Nov 2018. [TR] Jiechao Xiong, Qing Wang, Zhuoran Yang, Peng Sun, Lei Han, Yang Zheng, Haobo Fu, Tong Zhang, Ji Liu, Han Liu. Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space , Tech Report arXiv:1810.06394, Oct 2018. [TR] Lei Han, Kean Ming Tan, Ting Yang, Tong Zhang. Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression, Tech Report arXiv:1604.08098, April 2016. |
|
[184] Jialei Wang and Tong Zhang. Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations, JMLR, 2019. [183] Luo Luo and Cheng Chen and Zhihua Zhang and Wu-Jun Li and Tong Zhang. Robust Frequent Directions with Application in Online Learning, JMLR, 2019. [182] Wenhan Luo and Peng Sun and Fangwei Zhong and Wei Liu and Tong Zhang and Yizhou Wang. End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. [181] Meng Fang and Cheng Zhou and Bei Shi and Boqing Gong and Jia Xu and Tong Zhang. DHER: Hindsight Experience Replay for Dynamic Goals, ICLR 2019. |
|
[180] Zhaopeng Tu and Yang Liu and Shuming Shi and Tong Zhang. Learning to Remember Translation History with a Continuous Cache. , Transactions of the Association for Computational Linguistics, 6:407--420, 2018. [179] Kean Ming Tan and Zhaoran Wang and Han Liu and Tong Zhang. Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow, JRSS-B, 80:1057--1086, 2018. [178] Kean Ming Tan and Zhaoran Wang and Tong Zhang and Han Liu and R. Dennis Cook. A Convex Formulation For High-Dimensional Sparse Sliced Inverse Regression, Biometrika, 105:769--782, 2018. [176] Dong Dai and Lei Han and Ting Yang and Tong Zhang. Bayesian Model Averaging with Exponentiated Least Squares Loss , IEEE Trans. Info. Theory, 2018. [175] Xiao-Tong Yuan and Ping Li and Tong Zhang. Gradient Hard Thresholding Pursuit, JMLR , 18:1--43, 2018. [174] Lei Han and Lei Li and Feng Wen and Lei Zhong and Tong Zhang and Xiu-Feng Wan. Graph-Guided Multi-Task Sparse Learning Model: a Method for Identifying Antigenic Variants of Influenza A(H3N2) Virus, Bioinformatics , 2018. [173] Cong Fang and Chris Junchi Li and Zhouchen Lin and Tong Zhang. Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator, NIPS 2018. [172] Jianfei Chen and Jun Zhu and Yee Whye Teh and Tong Zhang. Stochastic Expectation Maximization with Variance Reduction, NIPS 2018. [171] Conghui Tan and Tong Zhang and Shiqian Ma and Ji Liu. Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity, NIPS 2018. [170] Hanlin Tang and Shaoduo Gan and Ce Zhang and Tong Zhang and Ji Liu. Communication Compression for Decentralized Training, NIPS 2018. [169] Jianqiao Wangni and Jialei Wang and Ji Liu and Tong Zhang. Gradient Sparsification for Communication-Efficient Distributed Optimization, NIPS 2018. [168] Qing Wang and Jiechao Xiong and Lei Han and Peng Sun and Han Liu and Tong Zhang. Exponentially Weighted Imitation Learning for Batched Historical Data, NIPS 2018. [167] Jiaxiang Wu and Weidong Huang and Junzhou Huang and Tong Zhang. Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization, ICML 2018. [166] Rie Johnson and Tong Zhang. Composite Functional Gradient Learning of Generative Adversarial Models, ICML 2018. [full version] [165] Qiang Sun and Kean Ming Tan and Han Liu and Tong Zhang. Graphical Nonconvex Optimization via an Adaptive Convex Relaxation, ICML 2018. [164] Lei Han and Yiheng Huang and Tong Zhang. Candidates vs. Noises Estimation for Large Multi-Class Classification Problem, ICML 2018. [163] Kaiqing Zhang and Zhuoran Yang and Han Liu and Tong Zhang and Tamer Basar. Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents, ICML 2018. |
|
[162] Chris J. Li, Mengdi Wang, Han Liu, Tong Zhang. Near-Optimal Stochastic Approximation for Online Principal Component Estimation , Mathematical Programming, 2017. [161] Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang. A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization, JMLR , 18:1-52, 2017. [160] Tuo Zhao and Han Liu and Tong Zhang. Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory, Annals of Statistics, 2017. (arXiv:1412.7477) [159]Jianqing Fan and Han Liu and Qiang Sun and Tong Zhang. I-LAMM for Sparse Learning: Simultaneous Control of Algorithmic Complexity and Statistical Error, Annals of Statistics, 2017. (arXiv:1507.01037) [158] Jun Song and Jun Xiao and Fei Wu and Haishan Wu and Tong Zhang and Zhongfei Zhang and Wenwu Zhu. Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion, IEEE Transactions on Knowledge and Data Engineering, 2017. [157] Chris Junchi Li and Mengdi Wang and Tong Zhang. Diffusion Approximations for Online Principal Component Estimation and Global Convergence, NIPS 2017. [156] Xingguo Li and Lin Yang and Jason Ge and Jarvis Haupt and Tong Zhang and Tuo Zhao. On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning, NIPS 2017. [155] Wenpeng Zhang and Peilin Zhao and Wei Liu and Steven Hoi and Wenwu Zhu and Tong Zhang. Projection-Free Distributed Online Learning in Networks, ICML 2017. [154] Jialei Wang and Mladen Kolar and Nathan Srebro and Tong Zhang. Efficient Distributed Learning with Sparsity, ICML 2017. [full version] [153] Rie Johnson and Tong Zhang. Deep Pyramid Convolutional Neural Networks for Text Categorization, ACL 2017. [ code ] |
[144] 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. [143] Daniel Vainsencher and Han Liu and Tong Zhang. Local Smoothness in Variance Reduced Optimization, NIPS, 2015. [142] Zheng Qu and Peter Richtárik and Tong Zhang. Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling. NIPS, 2015. [full version] [141] Rie Johnson and Tong Zhang. Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding. NIPS, 2015. [code] [140] Peilin Zhao and Jinwei Yang and Tong Zhang and Ping Li. Adaptive Stochastic Alternating Direction Method of Multipliers. ICML, 2015. [139] Peilin Zhao and Tong Zhang. Stochastic Optimization with Importance Sampling for Regularized Loss Minimization, ICML, 2015. [full version] [138] Rie Johnson and Tong Zhang. Effective Use of Word Order for Text Categorization with Convolutional Neural Networks, NAACL, 2015. [137] Tian Tian and Jun Zhu and Fen Xia and Xin Zhuang and Tong Zhang. Crowd Fraud Detection in Internet Advertising, WWW, 2015. |
|
[136] Lin Xiao and Tong Zhang. A Proximal Stochastic Gradient Method with Progressive Variance Reduction, Siam Journal on Optimization, 24:2057-2075, 2014. [135] 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. [134] Xiaotong Yuan and Tong Zhang. Partial Gaussian Graphical Model Estimation, IEEE Trans. Info. Th., 60:1673-1687, 2014. [133] Daniel Hsu and Sham M. Kakade and Tong Zhang. Random Design Analysis of Ridge Regression, Foundations of Computational Mathematics, March 2014. [132] 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. [131] Dong Dai and Philippe Rigollet and Lucy Xia and Tong Zhang. Aggregation of Affine Estimators, Electron. J. Statist, 8:302--327, 2014. [130] Mu Li and Tong Zhang and Yuqiang Chen and Alexander Smola. Efficient Mini-batch Training for Stochastic Optimization , KDD 2014. [129] Chen Cheng and Fen Xia and Tong Zhang and Irwin King and Michael R. Lyu. Gradient Boosting Factorization Machines , RecSys 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]. |
|
[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] |
|
[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) |
|
[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. |
|
[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. |
|
[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. |
|
[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. |
|
[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. |
|
[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. |
|
[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. |
|
[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]) |
|
[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]) |
|
[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. |
|
T. Zhang, G. Golub, and K.H. Law. Subspace iterative
methods for eigenvalue problems. Lin. Alg. and Appl.,
294:239-258, 1999. |