Department of Mathematics, HKUST  

  ZHANG, Tong    張潼

  PhD Stanford University

Office: Room 3455  

Telephone: 3469 2681  

Email: tongzhang@ust.hk  

URL:
https://www.math.hkust.edu.hk/~tongzhang   
  Chair Professor

Research Interests

  • Machine Learning algorithms and theory, Statistical Methods for Big Data and their applications

       Selected Publications

Full Publication List [HKUST Scholarly Publications]  

Article   

  1. Graph-guided multi-task sparse learning model: A method for identifying antigenic variants of influenza A(H3N2) virus
    • Author(s): Han, Lei; Li, Lei; Wen, Feng; Zhong, Lei; Zhang, Tong; Wan, Xiu Feng
    • Source: Bioinformatics, v. 35, (1), 2019, p. 77-87
    • Year: 2019

  2. Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models
    • Author(s): Tan, K.M.; Lu, Junwei; Zhang, Tong; Liu, Han
    • Source: Journal of Machine Learning Research, v. 20, August 2019, article number 119, p. 1-39
    • Year: 2019

  3. A convex formulation for high-dimensional sparse sliced inverse regression
    • Author(s): Tan, Kean Ming; Wang, Zhaoran; Zhang, Tong; Liu, Han; Cook, R.Dennis
    • Source: Biometrika, v. 105, (4), December 2018, p. 769-782
    • Year: 2018

  4. Sparse generalized eigenvalue problem: optimal statistical rates via truncated Rayleigh flow
    • Author(s): Tan, Kean Ming; Wang, Zhaoran; Liu, Han; Zhang, Tong
    • Source: Journal of the Royal Statistical Society. Series B: Statistical Methodology, v. 80, (5), November 2018, p. 1057-1086
    • Year: 2018

  5. Gradient hard thresholding pursuit
    • Author(s): Yuan, Xiao Tong; Li, Ping; Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 18, May 2018, article number 166, p. 1-43
    • Year: 2018

  6. Bayesian Model Averaging with Exponentiated Least Squares Loss
    • Author(s): Dai, Dong; Han, Lei; Yang, Ting; Zhang, Tong
    • Source: IEEE Transactions on Information Theory, v. 64, (5), May 2018, p. 3331-3345
    • Year: 2018

  7. I-LAMM for sparse learning: Simultaneous control of algorithmic complexity and statistical error
    • Author(s): Fan, Jianqing; Liu, Han; Sun, Qiang; Zhang, Tong
    • Source: Annals of Statistics, v. 46, (2), April 2018, p. 814-841
    • Year: 2018

  8. Pathwise coordinate optimization for sparse learning: Algorithm and theory
    • Author(s): Zhao, Tuo; Liu, Han; Zhang, Tong
    • Source: Annals of Statistics, v. 46, (1), February 2018, p. 180-218
    • Year: 2018

  9. Near-optimal stochastic approximation for online principal component estimation
    • Author(s): Li, C.J.; Wang, M.; Liu, H.; Zhang, T.
    • Source: Mathematical Programming, v. 167, (1), January 2018, p. 75-97
    • Year: 2018

  10. Learning to remember translation history with a continuous cache
    • Author(s): Tu, Zhaopeng; Liu, Yang; Shi, Shuming; Zhang, Tong
    • Source: Transactions of the Association for Computational Linguistics, v. 6, 2016, p. 407-420
    • Year: 2018

  11. A general distributed dual coordinate optimization framework for regularized loss minimization
    • Author(s): Zheng, S.; Wang, J.; Xia, F.; Xu, W.; Zhang, T.
    • Source: Journal of Machine Learning Research, v. 18, October 2017, article number 1, p. 1-52
    • Year: 2017

  12. Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion
    • Author(s): Song, Jun; Xiao, Jun; Wu, Fei; Wu, Haishan; Zhang, Tong; Zhang, Zhongfei Mark; Zhu, Wenwu
    • Source: IEEE Transactions on Knowledge and Data Engineering, v. 29, (9), September 2017, article number 7917325, p. 1888-1901
    • Year: 2017

  13. Towards more efficient SPSD matrix approximation and CUR matrix decomposition
    • Author(s): Wang, S.; Zhang, Z.; Zhang, T.
    • Source: Journal of Machine Learning Research, v. 17, December 2016, article number 210, p. 1-49
    • Year: 2016

  14. Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
    • Author(s): Shalev-Shwartz, Shai; Zhang, Tong
    • Source: Mathematical Programming, v. 155, (1-2), January 2016, p. 105-145
    • Year: 2016

  15. Learning sparse low-threshold linear classifiers
    • Author(s): Sabato, Sivan; Shalev-Shwartz, Shai; Srebro, Natwhan; Hsu, Daniel; Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 16, July 2015, p. 1275-1304
    • Year: 2015

  16. A proximal stochastic gradient method with progressive variance reduction
    • Author(s): Xiao, Lin; Zhang, Tong
    • Source: SIAM Journal on Optimization, v. 24, (4), 2014, p. 2057-2075
    • Year: 2014

  17. Learning nonlinear functions using regularized greedy forest
    • Author(s): Johnson, Rie; Zhang, Tong
    • Source: IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 36, (5), 2014, p. 942-54
    • Year: 2014

  18. Partial Gaussian graphical model estimation
    • Author(s): Yuan, Xiao-Tong; Zhang, Tong
    • Source: IEEE Transactions on Information Theory, v. 60, (3), March 2014, article number 6698361, p. 1673-1687
    • Year: 2014

  19. Optimal computational and statistical rates of convergence for sparse nonconvex learning problems
    • Author(s): Wang, Zhaoran; Liu, Han; Zhang, Tong
    • Source: Annals of Statistics, v. 42, (6), December 2014, p. 2164-2201
    • Year: 2014

  20. Random Design Analysis of Ridge Regression
    • Author(s): Hsu, Daniel; Kakade, Sham M.; Zhang, Tong
    • Source: Foundations of Computational Mathematics, v. 14, (3), June 2014, p. 569-600
    • Year: 2014

  21. A Joint Matrix Completion and Filtering Model for Influenza Serological Data Integration
    • Author(s): Yuan, Xiao-Tong; Zhang, Tong; Wan, Xiu-Feng
    • Source: PLoS ONE, v. 8, (7), July 2013, article number e69842, p. 1-12
    • Year: 2013

  22. A proximal-gradient homotopy method for the sparse least-squares problem
    • Author(s): Xiao, Lin; Zhang, Tong
    • Source: SIAM Journal on Optimization, v. 23, (2), 2013, p. 1062-1091
    • Year: 2013

  23. Stochastic Dual Coordinate Ascent methods for regularized loss minimization
    • Author(s): Shalev-Shwartz, Shai; Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 14, (1), February 2013, p. 567-599
    • Year: 2013

  24. Multi-stage convex relaxation for feature selection
    • Author(s): Zhang, Tong
    • Source: Bernoulli, v. 19, (5B), November 2013, p. 2277-2293
    • Year: 2013

  25. Truncated power method for sparse eigenvalue problems
    • Author(s): Yuan, Xiao-Tong; Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 14, (1), 2013, p. 899-925
    • Year: 2013

  26. A tail inequality for quadratic forms of subgaussian random vectors
    • Author(s): Hsu, Daniel; Kakade, Sham M.; Zhang, Tong
    • Source: Electronic Communications in Probability, v. 17, November 2012, article number 52, p. 1-6
    • Year: 2012

  27. A General theory of concave regularization for high-dimensional sparse estimation problems
    • Author(s): Zhang, Cun-Hui; Zhang, T.
    • Source: Statistical Science, v. 27, (4), November 2012, p. 576-593
    • Year: 2012

  28. A spectral algorithm for learning Hidden Markov Models
    • Author(s): Hsu, Daniel; Kakade, Sham M.; Zhang, Tong
    • Source: Journal of Computer and System Sciences, v. 78, (5), September 2012, p. 1460-1480
    • Year: 2012

  29. Tail inequalities for sums of random matrices that depend on the intrinsic dimension
    • Author(s): Hsu, Daniel; Kakadey, Sham M.; Zhang, Tong
    • Source: Electronic Communications in Probability, v. 17, March 2012, article number 14, p. 1-13
    • Year: 2012

  30. Deviation optimal learning using greedy q-aggregation
    • Author(s): Dai, Dong; Rigollet, Philippe; Zhang, Tong
    • Source: Annals of Statistics, v. 40, (3), June 2012, p. 1878-1905
    • Year: 2012

  31. Identifying antigenicity-associated sites in highly pathogenic H5N1 influenza virus hemagglutinin by using sparse learning
    • Author(s): Cai, Zhipeng; Ducatez, Mariette F.; Yang, Jialiang; Zhang, Tong; Long, Li-Ping; Boon, Adrianus C; Webby, Richard J.; Wan, Xiu-Feng
    • Source: Journal of Molecular Biology, v. 422, (1), September 2012, p. 145-155
    • Year: 2012

  32. Robust matrix decomposition with sparse corruptions
    • Author(s): Hsu, Daniel; Kakade, Sham M.; Zhang, Tong
    • Source: IEEE Transactions on Information Theory, v. 57, (11), November 2011, article number 5934412, p. 7221-7234
    • Year: 2011

  33. Learning with structured sparsity
    • Author(s): Huang, Junzhou; Zhang, Tong; Metaxas, Dimitris
    • Source: Journal of Machine Learning Research, v. 12, November 2011, p. 3371-3412
    • Year: 2011

  34. Adaptive forward-backward greedy algorithm for learning sparse representations
    • Author(s): Zhang, Tong
    • Source: IEEE Transactions on Information Theory, v. 57, (7), July 2011, article number 5895111, p. 4689-4708
    • Year: 2011

  35. Sparse recovery with orthogonal matching pursuit under RIP
    • Author(s): Zhang, Tong
    • Source: IEEE Transactions on Information Theory, v. 57, (9), 2011, article number 6006641, p. 6245-6221
    • Year: 2011

  36. Integrative analysis of many weighted Co-Expression networks using tensor computation
    • Author(s): Li, Wenyuan; Liu, Chun-Chi; Zhang, Tong; Li, Haifeng; Waterman, Michael S.; Zhou, Xianghong Jasmine
    • Source: PLoS Computational Biology, v. 7, (6), June 2011, article number e1001106
    • Year: 2011

  37. Concepts and applications for influenza antigenic cartography
    • Author(s): Cai, Zhipeng; Zhang, Tong; Wan, Xiu-Feng
    • Source: Influenza and other respiratory viruses, v. 5 Suppl 1, May 2011, p. 204-207
    • Year: 2011

  38. Trading accuracy for sparsity in optimization problems with sparsity constraints
    • Author(s): Shalev-Shwartz, Shai; Srebro, Nathan; Zhang, Tong
    • Source: SIAM Journal on Optimization, v. 20, (6), 2010, p. 2807-2832
    • Year: 2010

  39. The benefit of group sparsity
    • Author(s): Huang, Junzhou; Zhang, T.
    • Source: Annals of Statistics, v. 38, (4), August 2010, p. 1978-2004
    • Year: 2010

  40. A computational framework for influenza antigenic cartography
    • Author(s): Cai, Zhipeng; Zhang, Tong; Wan, Xiu-Feng
    • Source: PLoS Computational Biology, v. 6, (10), October 2010, article number 1000949
    • Year: 2010

  41. Analysis of multi-stage convex relaxation for sparse regularization
    • Author(s): Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 11, March 2010, p. 1081-1107
    • Year: 2010

  42. Classifying search quries using the web as a source of knowledge.
    • Author(s): Gabrilovich, Evgeniy; Broder, Andrei; Fontoura, Marcus; Joshi, Amruta; Josifovski, Vanja; Riedel, Lance; Zhang, Tong
    • Source: ACM Transactions on the Web, v. 3, (2), April 2009, article number 5
    • Year: 2009

  43. Some sharp performance bounds for least squares regression with L1 regularization
    • Author(s): Zhang, T.
    • Source: Annals of Statistics, v. 37, (5A), 2009, p. 2109-2144
    • Year: 2009

  44. Sparse Online Learning via Truncated Gradient
    • Author(s): Langford, J.; Li, L.; Zhang, T.
    • Source: Journal of Machine Learning Research, v. 10, January 2009, p. 777-801
    • Year: 2009

  45. On the consistency of feature selection using greedy least squares regression
    • Author(s): Zhang, T.
    • Source: Journal of Machine Learning Research, v. 10, January 2009, p. 555-568
    • Year: 2009

  46. Statistical analysis of Bayes optimal subset ranking
    • Author(s): Cossock, David; Zhang, Tong
    • Source: IEEE Transactions on Information Theory, v. 54, (11), 2008, p. 5140-5154
    • Year: 2008

  47. An online relevant set algorithm for statistical machine translation
    • Author(s): Tillmann, Christoph; Zhang, Tong
    • Source: IEEE Transactions on Audio, Speech and Language Processing, v. 16, (7), September 2008, p. 1274-1286
    • Year: 2008

  48. Graph-based semi-supervised learning and spectral kernel design
    • Author(s): Johnson, Ric; Zhang, Tong
    • Source: IEEE Transactions on Information Theory, v. 54, (1), January 2008, p. 275-288
    • Year: 2008

  49. A block bigram prediction model for statistical machine translation
    • Author(s): Tillmann, Christoph; Zhang, Tong
    • Source: ACM Transactions on Speech and Language Processing, v. 4, (3), July 2007, article number 1255172
    • Year: 2007

  50. On the effectiveness of Laplacian normalization for graph semi-supervised learning
    • Author(s): Johnson, Rie; Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 8, July 2007, p. 1489-1517
    • Year: 2007

  51. Information theoretical upper and lower bounds for statistical estimation
    • Author(s): Zhang, T.
    • Source: IEEE Transactions on Information Theory, v. 52, (4), April 2006, p. 1307-1321
    • Year: 2006

  52. From ε-entropy to KL-entropy: Analysis of minimum information complexity density estimation
    • Author(s): Zhang, Tong
    • Source: Annals of Statistics, v. 34, (5), October 2006, p. 2180-2210
    • Year: 2006

  53. Learning bounds for kernel regression using effective data dimensionality
    • Author(s): Zhang, Tong
    • Source: Neural Computation, v. 17, (9), September 2005, p. 2077-2098
    • Year: 2005

  54. A framework for learning predictive structures from multiple tasks and unlabeled data.
    • Author(s): Ando, Rie Kubota; Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 6, November 2005, p. 1817-1853
    • Year: 2005

  55. Boosting with early stopping: Convergence and consistency
    • Author(s): Zhang, Tong; Yu, Bin
    • Source: Annals of Statistics, v. 33, (4), August 2005, p. 1538-1579
    • Year: 2005

  56. Generalization error bounds for Bayesian mixture algorithms
    • Author(s): Meir, R.; Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 4, (5), July 2004, p. 839-860
    • Year: 2004

  57. Text categorization for a comprehensive time-dependent benchmark
    • Author(s): Damerau, Fred J.; Zhang, Tong; Weiss, Sholom M.; Indurkhya, Nitin
    • Source: Information Processing and Management, v. 40, (2), March 2004, p. 209-221
    • Year: 2004

  58. Greedy algorithms for classification - Consistency, convergence rates, and adaptivity
    • Author(s): Mannor, Shie; Meir, Ron; Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 4, (4), May 2004, p. 713-742
    • Year: 2004

  59. Statistical behavior and consistency of classification methods based on convex risk minimization
    • Author(s): Zhang, Tong
    • Source: Annals of Statistics, v. 32, (1), February 2004, p. 56-85
    • Year: 2004

  60. Statistical analysis of some multi-category large margin classification methods
    • Author(s): Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 9, 2004, p. 12251250-5
    • Year: 2004

  61. Leave-one-out bounds for kernel methods
    • Author(s): Zhang, Tong
    • Source: Neural Computation, v. 15, (6), 2003, p. 1397-1437
    • Year: 2003

  62. Sequential greedy approximation for certain convex optimization problems
    • Author(s): Zhang, Tong
    • Source: IEEE Transactions on Information Theory, v. 49, (3), March 2003, p. 682-691
    • Year: 2003

  63. Two-Sided Arnoldi and Nonsymmetric Lanczos Algorithms
    • Author(s): Cullum, Jane; Zhang, Tong
    • Source: SIAM Journal on Matrix Analysis and Applications, v. 24, (2), December 2002, p. 303-319
    • Year: 2002

  64. On the dual formulation of regularized linear systems
    • Author(s): Zhang, Tong
    • Source: Machine Learning, v. 46, (1-3), January 2002, p. 91-129
    • Year: 2002

  65. Recommender Systems Using Linear Classifiers
    • Author(s): Zhang, Tong; Iyengar, Vijay S.
    • Source: Journal of Machine Learning Research, v. 2, (3), 2002, p. 313-334
    • Year: 2002

  66. Text Chunking based on a Generalization of Winnow
    • Author(s): Zhang, Tong; Damerau, Fred; Johnson, David
    • Source: Journal of Machine Learning Research, v. 2, (4), 2002, p. 615-638
    • Year: 2002

  67. Covering Number Bounds of Certain Regularized Linear Function Classes
    • Author(s): Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 2, (3), 2002, p. 527-550
    • Year: 2002

  68. Approximation bounds for some sparse kernel regression algorithms
    • Author(s): Zhang, Tong
    • Source: Neural Computation, v. 14, (12), December 2002, p. 3013-3042
    • Year: 2002

  69. On the consistency of instantaneous rigid motion estimation
    • Author(s): Zhang, Tong; Tomasi, Carlo
    • Source: International Journal of Computer Vision, v. 46, (1), 2002, article number 390064, p. 51-79
    • Year: 2002

  70. A decision-tree-based symbolic rule induction system for text categorization
    • Author(s): Johnson, D.E.; Oles, F.J.; Zhang, Tong; Goetz, T.
    • Source: IBM Systems Journal, v. 41, (3), 2002, p. 428-437
    • Year: 2002

  71. Rank-one approximation to high order tensors
    • Author(s): Zhang, Tong; Golub, Gene H.
    • Source: SIAM Journal on Matrix Analysis and Applications, v. 23, (2), November 2001, p. 534-550
    • Year: 2001

  72. Text Categorization Based on Regularized Linear Classification Methods
    • Author(s): Zhang, Tong; Oles, Frank J.
    • Source: Information Retrieval, v. 4, (1), April 2001, p. 5-31
    • Year: 2001

  73. A Method for Reduced-Order Modeling and Simulation of Large Interconnect Circuits and its Application to PEEC Models with Retardation
    • Author(s): Cullum, J; Ruehli, A; Zhang, T.
    • Source: IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, v. 47, (4), April 2000, p. 261-273
    • Year: 2000

  74. Subspace iterative methods for eigenvalue problems
    • Author(s): Zhang, T.; Golub, G.H.; Law, K.H.
    • Source: Linear Algebra and Its Applications, v. 294, (1-3), June 1999, p. 239-258
    • Year: 1999

  75. On the Homotopy Method for Perturbed Symmetric Generalized Eigenvalue Problems
    • Author(s): Zhang, T.; Law, K.H.; Golub, G.H.
    • Source: SIAM Journal of Scientific Computing, v. 19, (5), September 1998, p. 1625-1645
    • Year: 1998

  76. Eigenvalue perturbation and the generalized Krylov subspace method
    • Author(s): Zhang, T.; Golub, G.H.; Law, K.H.
    • Source: Applied Numerical Mathematics, v. 27, (2), June 1998, p. 185-202
    • Year: 1998

  77. Densities of self-similar measures on the line
    • Author(s): Strichartz, Robert S.; Taylor, Arthur; Zhang, Tong
    • Source: Experimental Mathematics, v. 4, (2), 1995, p. 101-128
    • Year: 1995

Book   

  1. Text mining: Predictive methods for analyzing unstructured information
    • Author(s): Weiss, S.M.; Indurkhya, N.; Zhang, Tong; Damerau, F.J.
    • Source: Text Mining: Predictive Methods for Analyzing Unstructured Information / 2005, p. 1-237
    • Year: 2005

Book chapter   

  1. Fundamental statistical techniques
    • Author(s): Zhang, Tong
    • Source: Handbook of Natural Language Processing / Nitin Indurkhya, Fred J. Damerau. Boca Raton, London, Now York : CRC Press, 2010, p. 189-204, Ch. 9. Second Edition. Book Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition Series.
    • Year: 2010

  2. Performance analysis and evaluation
    • Author(s): Weiss, Sholom M.; Zhang, Tong
    • Source: The Handbook of Data Mining /Edited by Nong Ye. Mahwah, New Jersey; London : LAWRENCE ERLBAUM ASSOCIATES, 2003, p. 425-440, Ch. 17. Series: Human Factors and Ergonomics.
    • Year: 2003

Conference paper   

  1. DHER: Hindsight experience replay for dynamic goals
    • Author(s): Fang, Meng; Zhou, Cheng; Shi, Bei; Gong, Boqing; Xu, Jia; Zhang, Tong
    • Source: , 8th International Conference on Learning Representations, New Orleans, Louisiana, United States, 6-9 May 2019
    • Year: 2019

  2. Reinforced Training Data Selection for Domain Adaptation
    • Author(s): Liu, Miaofeng; Song, Yan; Zou, Hongbin; Zhang, Tong
    • Source: 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), 2019, p. 1957-1968
    • Year: 2019

  3. Stochastic expectation maximization with variance reduction
    • Author(s): Chen, J.; Zhu, J.; Teh, Y.W.; Zhang, T.
    • Source: Advances in Neural Information Processing Systems, v. 31, 2018, p. 7967-7977
    • Year: 2018

  4. Stochastic primal-dual method for empirical risk minimization with O(1) per-iteration complexity
    • Author(s): Tan, C.; Ma, S.; Zhang, T.; Liu, J.
    • Source: Advances in Neural Information Processing Systems, v. 31, 2018, p. 8366-8375
    • Year: 2018

  5. Near-optimal non-convex optimization via stochastic path integrated differential estimator
    • Author(s): Fang, C.; Li, C.J.; Lin, Z.; Zhang, T.
    • Source: Advances in Neural Information Processing Systems, v. 31, 2018, p. 689-699
    • Year: 2018

  6. Gradient sparsification for communication-efficient distributed optimization
    • Author(s): Wangni, J.; Liu, J.; Wang, J.; Zhang, T.
    • Source: Advances in Neural Information Processing Systems, v. 31, 2018, p. 1299-1309
    • Year: 2018

  7. Graphical nonconvex optimization via an adaptive convex relaxation
    • Author(s): Sun, Q.; Tan, K.M.; Liu, H.; Zhang, T.
    • Source: Proceedings of International Conference on Machine Learning, ICML 2018, v. 11, 2018, p. 7638-7645, Series: Proceedings of Machine Learning Research, v. 80
    • Year: 2018

  8. Communication compression for decentralized training
    • Author(s): Tang, Hanlin; Gan, Shaoduo; Zhang, Ce; Zhang, Tong; Liu, Ji
    • Source: Advances in Neural Information Processing Systems, v. 31, 2018, p. 7652-7662
    • Year: 2018

  9. An algorithmic framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-gradient method
    • Author(s): Shen, Li; Sun, Peng; Wang, Yitong; Liu, Wei; Zhang, Tong
    • Source: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, v. 10, 2018, p. 7376-7385, Series: Proceedings of Machine Learning Research, v. 80
    • Year: 2018

  10. Safe element screening for submodular function minimization
    • Author(s): Zhang, Weizhong; Hong, Bin; Ma, Lin; Liu, Wei; Zhang, Tong
    • Source: Proceedings of International Conference on Machine Learning, ICML 2018, v. 13, 2018, p. 9211-9220, Series: Proceedings of Machine Learning Research, v. 80
    • Year: 2018

  11. Exponentially weighted imitation learning for batched historical data
    • Author(s): Wang, Qing; Xiong, Jiechao; Han, Lei; Sun, Peng; Liu, Han; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 31, 2018, p. 6288-6297
    • Year: 2018

  12. End-to-end active object tracking via reinforcement learning
    • Author(s): Luo, Wenhan; Sun, Peng; Zhong, Fangwei; Liu, Wei; Zhang, Tong; Wang, Yizhou
    • Source: Proceedings of International Conference on Machine Learning, ICML 2018, v. 7, 2018, p. 5191-5200, Series: Proceedings of Machine Learning Research. v. 80
    • Year: 2018

  13. Fully decentralized multi-agent reinforcement learning with networked agents
    • Author(s): Zhang, Kaiqing; Yang, Zhuoran; Liu, Han; Zhang, Tong; Bas¸ar, Tamer
    • Source: Proceedings of Machine Learning Research, v. 80, 2018, p. 5872-5881
    • Year: 2018

  14. Candidates vs. noises estimation for large multi-class classification problem
    • Author(s): Han, Lei; Huang, Yiheng; Zhang, Tong
    • Source: Proceedings of Machine Learning Research, v. 80, 2018, p. 1890-1899
    • Year: 2018

  15. Error compensated quantized sgd and its applications to large-scale distributed optimization
    • Author(s): Wu, Jiaxiang; Huang, Weidong; Huang, Junzhou; Zhang, Tong
    • Source: Proceedings of Machine Learning Research, v. 80, 2018, p. 5325-5333
    • Year: 2018

  16. Composite functional gradient learning of generative adversarial models
    • Author(s): Johnson, Rie; Zhang, Tong
    • Source: 35th International Conference on Machine Learning, ICML 2018, v. 80, 2018, p. 2371-2379
    • Year: 2018

  17. Deep pyramid convolutional neural networks for text categorization
    • Author(s): Johnson, Rie; Zhang, Tong
    • Source: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, v. 1, 2017, p. 562-570
    • Year: 2017

  18. Efficient distributed learning with sparsity
    • Author(s): Wang, Jialei; KoIar, Mladen; Srebro, Nathan; Zhang, Tong
    • Source: Proceedings of International Conference on Machine Learning, ICML 2017, v. 7, 2017, p. 5544-5563, Series: Proceedings of Machine Learning Research, v. 70
    • Year: 2017

  19. Diffusion approximations for online principal component estimation and global convergence
    • Author(s): Li, Chris Junchi; Wang, Mendgi; Liu, Han; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 30, 2017, p. 646-656
    • Year: 2017

  20. Projection-free distributed online learning in networks
    • Author(s): Zhang, Wenpeng; Zhao, Peilin; Zhu, Wenwu; Hoi, Steven C. H.; Zhang, Tong
    • Source: Proceedings of International Conference on Machine Learning, ICML 2017, v. 8, 2017, p. 6155-6166, Series: Proceedings of Machine Learning Research, v. 70
    • Year: 2017

  21. On quadratic convergence of DC proximal Newton algorithm in nonconvex sparse learning
    • Author(s): Li, Xingguo; Yang, Lin F.; Ge, Jason; Haupt, J.; Zhang, Tong; Zhao, Tuo
    • Source: Advances in Neural Information Processing Systems, 2017, p. 2743-2753
    • Year: 2017

  22. Supervised and semi-supervised text categorization using LSTM for region embeddings
    • Author(s): Johnson, Rie; Zhang, Tong
    • Source: Proceedings of International Conference on Machine Learning, ICML 2016, v. 2, 2016, p. 794-802, Series: Proceedings of Machine Learning Research, v. 48
    • Year: 2016

  23. Exact recovery of hard thresholding pursuit
    • Author(s): Yuan, Xiaotong; Li, Ping; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 29, 2016, p. 3565-3573
    • Year: 2016

  24. Learning additive exponential family graphical models via l2,1-norm regularized M-estimation
    • Author(s): Yuan, Xiao-Tong; Li, Ping; Zhang, Tong; Liu, Qingshan; Liu, Guangcan
    • Source: Advances in Neural Information Processing Systems, v. 29, 2016, p. 4374-4382
    • Year: 2016

  25. Sparse nonlinear regression: Parameter estimation under nonconvexity
    • Author(s): Yang, Zhuoran; Wang, Zhaoran; Liu, Han; Eldar, Yonina C.; Zhang, Tong
    • Source: Proceedings of International Conference on Machine Learning, ICML 2016, v. 5, 2016, p. 3668-3677, Series: Proceedings of Machine Learning Research, v. 48
    • Year: 2016

  26. Stochastic optimization with importance sampling for regularized loss minimization
    • Author(s): Zhao, Peillin; Zhang, Tong
    • Source: Proceedings of International Conference on Machine Learning, ICML 2015, v. 1, 2015, p. 1-9, Series: Proceedings of Machine Learning Research, v. 37
    • Year: 2015

  27. Semi-supervised convolutional neural networks for text categorization via region embedding
    • Author(s): Johnson, Rie; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 28, 2015, p. 919-927
    • Year: 2015

  28. Effective use of word order for text categorization with convolutional neural networks
    • Author(s): Johnson, Rie; Zhang, Tong
    • Source: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015, 2015, p. 103-112
    • Year: 2015

  29. Quartz: Randomized dual coordinate ascent with arbitrary sampling
    • Author(s): Qu, Zheng; Richtárik, Peter; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, 2015, p. 865-873
    • Year: 2015

  30. Local smoothness in variance reduced optimization
    • Author(s): Vainsencher, Daniel; Liu, Han; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 28, 2015, p. 2179-2187
    • Year: 2015

  31. Adaptive stochastic alternating direction method of multipliers
    • Author(s): Zhao, Peilin; Yang, Jinwei; Zhang, Tong; Li, Ping
    • Source: Proceedings of International Conference on Machine Learning, ICML 2015, v. 1, 2015, p. 69-77, Series: Proceedings of Machine Learning Research, v.37
    • Year: 2015

  32. Crowd fraud detection in internet advertising
    • Author(s): Tian, Tian; Zhu, Jun; Xia, Fen; Zhuang, Xin; Zhang, Tong
    • Source: Proceedings of the 24th International Conference on World Wide Web, 2015, p. 1100-1110
    • Year: 2015

  33. Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
    • Author(s): Shnlev-Shwartz, Shai; Zhang, Tong
    • Source: 31st International Conference on Machine Learning, ICML 2014, v. 1, 2014, p. 111-119, Series: Proceedings of Machine Learning Research, v. 32
    • Year: 2014

  34. Compressed counting meets compressed sensing
    • Author(s): Li, Ping; Zhang, Cun-Hui; Zhang, Tong
    • Source: Journal of Machine Learning Research, v. 35, 2014, p. 1058-1077
    • Year: 2014

  35. Communication-efficient distributed optimization using an approximate Newton-type method
    • Author(s): Shamir, Ohad; Srebro, Nathan; Zhang, Tong
    • Source: Proceedings of Machine Learning Research, v. 32, (2), 2014, p. 1000-1008
    • Year: 2014

  36. Gradient hard thresholding pursuit for sparsity-constrained optimization
    • Author(s): Yuan, Xiaotong; Li, Ping; Zhang, Tong
    • Source: Proceedings of Machine Learning Research, v. 32, (2), 2014, p. 127-135
    • Year: 2014

  37. Efficient mini-batch training for stochastic optimization
    • Author(s): Li, Mu; Zhang, Tong; Chen, Yuqiang; Smola, A.J.
    • Source: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, p. 661-670
    • Year: 2014

  38. A convergence rate analysis for LogitBoost, MART and their variant
    • Author(s): Sun, Peng; Zhang, Tong; Zhou, Jie
    • Source: Proceedings of Machine Learning Research, v. 32, (2), 2014, p. 3001-3009
    • Year: 2014

  39. High-dimensional joint sparsity random effects model for multi-task learning
    • Author(s): Balasubramanian, K.; Yu, K.; Zhang, T.
    • Source: Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, UAI 2013, 5013, p. 42-51
    • Year: 2013

  40. Accelerated mini-batch stochastic dual coordinate ascent
    • Author(s): Shalev-Shwartz, Shai; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 26, 2013, p. 1-8
    • Year: 2013

  41. Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
    • Author(s): Shamir, Ohad; Zhang, Tong
    • Source: Proceedings of Machine Learning Research, v. 28, (1), 2013, p. 71-79
    • Year: 2013

  42. Accelerating stochastic gradient descent using predictive variance reduction
    • Author(s): Johnson, Rie; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 26, 2013, p. 1-9
    • Year: 2013

  43. Selective labeling via error bound minimization
    • Author(s): Gu, Quanquan; Zhang, Tong; Ding, Chris; Han, Jiawei
    • Source: Advances in Neural Information Processing Systems, v. 25, (1), 2012, p. 323-331
    • Year: 2012

  44. A proximal-gradient homotopy method for the ℓ 1-regularized least-squares problem
    • Author(s): Xiao, Lin; Zhang, Tong
    • Source: Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012
    • Year: 2012

  45. Random design analysis of ridge regression
    • Author(s): Hsu, Daniel; Kakade, Sham M.; Zhang, Tong
    • Source: Proceedings of Machine Learning Research, v. 23, 2012, p. 9.1-9.24
    • Year: 2012

  46. Learning to search efficiently in high dimensions
    • Author(s): Li, Zhen; Ning, Huazhong; Cao, Liangliang; Zhang, Tong; Gong, Yihong; Huang, Thomas S.
    • Source: Advances in Neural Information Processing Systems, v. 24, 2011, p. 1-9
    • Year: 2011

  47. Greedy model averaging
    • Author(s): Dai, Dong; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 24, 2011, p. 1-9
    • Year: 2011

  48. Spectral methods for learning multivariate latent tree structure
    • Author(s): Anandkumar, Animashree; Chaudhuri, Kamalika; Hsu, Daniel; Kakade, Sham M.; Song, Le; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 24, 2011, p. 1-9
    • Year: 2011

  49. Efficient optimal learning for contextual bandits
    • Author(s): Dudik, M.; Hsu, D.; Kale, S.; Karampatziakis, N.; Langford, J.; Reyzin, L.; Zhang, T.
    • Source: Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011, 2011, p. 169-178
    • Year: 2011

  50. Image classification using supervector coding of local image descriptors
    • Author(s): Zhou, Xi; Yu, Kai; Zhang, Tong; Huang, Thomas S.
    • Source: Lecture Notes in Computer Science, v. 6315 LNCS, (5), 2010, p. 141-154, (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    • Year: 2010

  51. Agnostic active learning without constraints
    • Author(s): Beygelzimer, Alina; Hsu, Daniel; Langford, John; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 23, 2010, p. 1-9
    • Year: 2010

  52. Deep coding network
    • Author(s): Lin, Yuanqing; Zhang, Tong; Zhu, Shenghuo; Yu, Kai
    • Source: Advances in Neural Information Processing Systems, v. 23, 2010, p. 1-9
    • Year: 2010

  53. Improved local coordinate coding using local tangents
    • Author(s): Yu, K.; Zhang, Tong
    • Source: Proceedings, 27th International Conference on Machine Learning, ICML 2010, 2010, p. 1215-1222
    • Year: 2010

  54. Nonlinear learning using local coordinate coding
    • Author(s): Yu, K.; Zhang, T.; Gong, Y.
    • Source: Advances in Neural Information Processing Systems, v. 22, 2009, p. 2223-2231
    • Year: 2009

  55. Multi-label prediction via compressed sensing
    • Author(s): Hsu, D.; Kakade, S.M.; Langford, J.; Zhang, T.
    • Source: Advances in Neural Information Processing Systems, v. 22, 2009, p. 772-780
    • Year: 2009

  56. Learning with structured sparsity
    • Author(s): Huang, Junzhou; Zhang, Tong; Metaxas, D.
    • Source: ACM International Conference Proceeding Series, v. 382, 2009, article number 52
    • Year: 2009

  57. Learning nonlinear dynamic models
    • Author(s): Langford, J.; Salakhutdinov, R.; Zhang, T.
    • Source: Proceedings of the 26th International Conference On Machine Learning, ICML 2009, 2009, p. 593-600
    • Year: 2009

  58. A spectral algorithm for learning hidden Markov models
    • Author(s): Hsu, Daniel; Kakade, Sham M.; Zhang, Tong
    • Source: Conference on Learning Theory (COLT) Proceedings, 2009
    • Year: 2009

  59. Multi-stage convex relaxation for learning with sparse regularization
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 21, 2009, p. 1929-1936
    • Year: 2008

  60. Adaptive forward-backward greedy algorithm for sparse learning with linear models
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 21, 2009, p. 1921-1928
    • Year: 2008

  61. Sparse online learning via truncated gradient
    • Author(s): Langford, J.; Li, L.; Zhang, T.
    • Source: Advances in Neural Information Processing Systems, v. 21, 2008, p. 905-912
    • Year: 2008

  62. Epoch-Greedy algorithm for multi-armed bandits with side information
    • Author(s): Langford, John; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems (NIPS 2007), v. 20, 2007, p. 1-8
    • Year: 2007

  63. A general boosting method and its application to learning ranking functions for Web search
    • Author(s): Zheng, Z.; Zha, H.; Zhang, T.; Chapelle, O.; Chen, K.; Sun, G.
    • Source: Advances in Neural Information Processing Systems, v. 20, 2009, p. 1-8
    • Year: 2007

  64. Two-view feature generation model for semi-supervised learning
    • Author(s): Ando, Rie Kubota; Zhang, Tong
    • Source: ACM International Conference Proceeding Series, v. 227, 2007, p. 25-32
    • Year: 2007

  65. Robust classification of rare queries using web knowledge
    • Author(s): Broder, Andrei Z.; Fontoura, Marcus; Gabrilovich, Evgeniy; Joshi, Amruta; Josifovski, Vanja; Zhang, Tong
    • Source: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'07, 2007, p. 231-238
    • Year: 2007

  66. Margin based active learning
    • Author(s): Balcan, Maria-Florina; Broder, Andrei; Zhang, Tong
    • Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 4539 LNAI, 2007, p. 35-50
    • Year: 2007

  67. Learning on graph with Laplacian regularization
    • Author(s): Ando, Rie K.; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 19, 2007, p. 25-32
    • Year: 2006

  68. A discriminative global training algorithm for statistical MT
    • Author(s): Tillmann, Christoph; Zhang, Tong
    • Source: Proceedings of 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL 2006), v. 1, 2006, p. 721-728
    • Year: 2006

  69. Linear prediction models with graph regularization for Web-page categorization
    • Author(s): Zhang, Tong; Popescul, Alexandrin; Dom, Byron
    • Source: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, v. 2006, 2006, p. 821-826
    • Year: 2006

  70. Subset ranking using regression
    • Author(s): Cossock, David; Zhang, Tong
    • Source: Lecture Notes in Computer Science, v. 4005 LNAI, 2006, p. 605-619, including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
    • Year: 2006

  71. A high-performance semi-supervised learning method for text chunking
    • Author(s): Ando, Rie Kubota; Zhang, Tong
    • Source: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL-05), 2005, p. 1-9
    • Year: 2005

  72. Analysis of spectral kernel design based semi-supervised learning
    • Author(s): Zhang, Tong; Ando, Rie K.
    • Source: Advances in Neural Information Processing Systems, 2005, p. 1601-1608
    • Year: 2005

  73. A localized prediction model for statistical machine translation
    • Author(s): Tillmann, C.; Zhang, Tong
    • Source: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL-05), 2005, p. 557-564
    • Year: 2005

  74. Localized upper and lower bounds for some estimation problems
    • Author(s): Zhang, Tong
    • Source: Lecture Notes in Computer Science, v. 3559 LNAI, 2005, p. 516-530, (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    • Year: 2005

  75. Data dependent concentration bounds for sequential prediction algorithms
    • Author(s): Zhang, Tong
    • Source: Lecture Notes in Computer Science, v. 3559 LNAI, 2005, p. 173-187, (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    • Year: 2005

  76. TREC 2005 genomics track experiments at IBM watson
    • Author(s): Ando, Rie Kubota; Dredze, Mark; Zhang, Tong
    • Source: NIST Special Publication, 2005, p. 1-10
    • Year: 2005

  77. Solving large scale linear prediction problems using stochastic gradient descent algorithms
    • Author(s): Zhang, Tong
    • Source: Proceedings of International Conference on Machine Learning, ICML 2004, 2004, p. 919-926
    • Year: 2004

  78. On the convergence of MDL density estimation
    • Author(s): Zhang, Tong
    • Source: Lecture Notes in Artificial Intelligence, v. 3120, 2004, p. 315-330, (Subseries of Lecture Notes in Computer Science)
    • Year: 2004

  79. Column-generation boosting methods for mixture of kernels
    • Author(s): Bi, Jinbo; Zhang, Tong; Bennett, K.P.
    • Source: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2004, 2004, p. 521-526
    • Year: 2004

  80. Focused named entity recognition using machine learning
    • Author(s): Zhang, Li; Pan, Yue; Zhang, Tong
    • Source: Proceedings of Sheffield SIGIR Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004, p. 281-288
    • Year: 2004

  81. Support vector classification with input data uncertainty
    • Author(s): Bi, Jinbo; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 17, 2005
    • Year: 2004

  82. Class-size independent generalization analsysis of some discriminative multi-category classification methods
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 17, 2005, p. 13-16
    • Year: 2004

  83. Learning bounds for a generalized family of Bayesian posterior distributions
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 16, 2004, p. 1149-1156
    • Year: 2004

  84. An infinity-sample theory for multi-category large margin classification
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 16, 2004
    • Year: 2004

  85. An evaluation of over-the-counter medication sales for syndromic surveillance
    • Author(s): Campbell, Murray S.; Li, Chung-Sheng; Aggarwal, Charu; Naphade, Milind R.; Wu, Kun-Lung; Zhang, Tong
    • Source: , In ICDM 2004 Workshop on Life Sciences Data Mining
    • Year: 2004

  86. Howtogetachinesename (entity): Segmentation and combination issues.
    • Author(s): Jing, Hongyan; Florian, Radu; Luo, Xiaoqiang; Zhang, Tong; Abraham Ittycheriah
    • Source: Proceedings of the 2003 conference on Empirical methods in natural language processing, 2003, p. 200-207
    • Year: 2003

  87. Named entity recogintion through classifier combination
    • Author(s): Florian, Radu; Ittycheriah, Abe; Jing, Hongyan; Zhang, Tong
    • Source: Proceedings of the seventh conference on Natural language learning, CONLL 2003, v. 4, 2003, p. 168-171
    • Year: 2003

  88. On the Convergence of Boosting Procedures
    • Author(s): Zhang, Tong; Yu, Bin
    • Source: Proceedings oh Twentieth International Conference on Machine Learning, v. 2, 2003, p. 904-911
    • Year: 2003

  89. Data-dependent bounds for Bayesian mixture methods
    • Author(s): Meir, Ron; Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 15, 2003
    • Year: 2003

  90. Updating an nlp system to fit new domains: an empirical study on the sentence segmentation problem
    • Author(s): Zhang, Tong; Damerau, Fred; Johnson, David
    • Source: Proceedings of the seventh conference on Natural language learning, CONLL 2003, v. 4, 2003, p. 56-62
    • Year: 2003

  91. A robust risk minimization based named entity recognition system
    • Author(s): Zhang, Tong; Johnson, David
    • Source: Proceedings of the seventh conference on Natural language learning, CONLL 2003, v. 4, 2003, p. 204-207
    • Year: 2003

  92. Effective dimension and generalization of kernel learning
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 15, 2003
    • Year: 2002

  93. Generalization performance of some learning problems in hilbert functional spaces
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 14, 2002, p. 543-550
    • Year: 2002

  94. Statistical behavior and consistency of support vector machines, boosting, and beyond
    • Author(s): Zhang, Tong
    • Source: Proceedings of the Nineteenth International Conference on Machine Learning, 2002, p. 690-700
    • Year: 2002

  95. The consistency of greedy algorithms for classification
    • Author(s): Mannor, Shie; Meir, Ron; Zhang, Tong
    • Source: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), v. 2375, 2002, p. 319-333
    • Year: 2002

  96. Empirical study of recommender systems using linear classifiers
    • Author(s): Iyengar, V.S.; Zhang, Tong
    • Source: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), v. 2035, 2001, p. 16-27
    • Year: 2001

  97. A leave-one-out cross validation bound for kernel methods with applications in learning
    • Author(s): Zhang, Tong
    • Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 2111, 2001, p. 427-443
    • Year: 2001

  98. A sequential approximation bound for some sample-dependent convex optimization problems with applications in learning
    • Author(s): Zhang, Tong
    • Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 2111, 2001, p. 65-81
    • Year: 2001

  99. Convergence of large margin separable linear classification
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 13, 2001
    • Year: 2001

  100. Regularized winnow methods
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 13, 2001
    • Year: 2001

  101. Text chunking using regularized Winnow
    • Author(s): Zhang, Tong; Damera, Fred; Johnson, David
    • Source: roceedings of the 39th Annual Meeting on Association for Computational Linguistics, 2001, p. 539-546
    • Year: 2001

  102. A general greedy approximation algorithm with applications
    • Author(s): Zhang, Tong
    • Source: Advances in Neural Information Processing Systems, v. 14, 2002
    • Year: 2001

  103. Some sparse approximation bounds for regression problems
    • Author(s): Zhang, Tong
    • Source: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, 2001, p. 624-631
    • Year: 2001

  104. Active learning using adaptive resampling
    • Author(s): Iyengar, Vijay S.; Apte, Chidanand; Zhang, Tong
    • Source: Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000, p. 91-98
    • Year: 2000

  105. Large margin Winnow methods for text categorization
    • Author(s): Zhang, Tong
    • Source: , 6th ACM SIGKDD international conference on Knowledge discovery and data mining, Boston, MA, USA, 20-23 August 2000
    • Year: 2000

  106. A probability analysis on the value of unlabeled data for classification problems
    • Author(s): Zhang, Tong; Oles, Frank J.
    • Source: , 7th International Conference on Machine Learning (ICML 2000), Stanford, CA, USA, 29 June - 2 July 2000
    • Year: 2000

  107. Theoretical analysis of a class of randomized regularization methods
    • Author(s): Zhang, Tong
    • Source: Proceedings of the Annual ACM Conference on Computational Learning Theory, 1999, p. 156-163
    • Year: 1999

  108. Fast, robust, and consistent camera motion estimation
    • Author(s): Zhang, Tong; Tomasi, Carlo
    • Source: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 1, 1999, article number 786934, p. 164-170
    • Year: 1999

  109. Some theoretical results concerning the convergence of composition of regularizated linear functions
    • Author(s): Zhang, Tong
    • Source: Proceedings of the 12th International Conference on Neural Information Processing Systems, 1999, p. 370-376
    • Year: 1999

  110. A linear algorithm for optimal context clustering with application to bi-level image coding
    • Author(s): Greene, Daniel; Yao, Frances; Zhang, Tong
    • Source: Proceedings: 1998 International Conference on Image Processing, ICIP98, v. 1, 1998, article number 723301, p. 508-511
    • Year: 1998

  111. Model reduction for peec models including retardation
    • Author(s): Cullurn, Jane; Ruelili, Albert E.; Zhang, Tong
    • Source: Proceedings of the 1998 IEEE 7th Topical Meeting on Electrical Performance of Electronic Packaging, 1998, p. 287-290
    • Year: 1998

  112. Compression by model combination
    • Author(s): Zhang, Tong
    • Source: Data Compression Conference Proceedings, 1998, p. 319-328
    • Year: 1998

  113. A progressive ziv-lempel algorithm for image compression
    • Author(s): Greene, Daniel; Vishwanath, Mohan; Yao, Frances; Zhang, Tong
    • Source: International Conference on Compression and Complexity of Sequences Proceedings, 1998, p. 136-144
    • Year: 1997

  114. Progressive Ziv-Lempel Encoding of Synthetic Images
    • Author(s): Greene, Daniel; Vishwanath, Mohan; Yao, Frances; Zhang, Tong
    • Source: Proceedings of Data Compression Conference (DCC '97), article number 582099, p. 441-441
    • Year: 1997

  115. Optimal surface smoothing as filter design
    • Author(s): Taubin, Gabriel; Zhang, Tong; Golub, Gene
    • Source: European Conference on Computer Vision, v. 1, 1996, p. 283-292, Part of the Lecture Notes in Computer Science book series (LNCS, volume 1064)
    • Year: 1996