Tong Zhang’s research papers

 

New:                                                                                                                                                                               

[TR] Tong Zhang. Forward-Backward Greedy Algorithm for Learning Sparse Representations. Rutgers Statistics Department Technical Report, April 2008.

[TR] Tong Zhang. Some Sharp Performance Bounds for Least Squares Regression with L1 Regularization. Rutgers Statistics Department Technical Report, Sept 2007.

 

2007:                                                                                                                                                                               

[71] Rie Johnson and Tong Zhang. Graph-based semi-supervised learning and spectral kernel design. IEEE Trans. Info. Theory, 2007, to appear.

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

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

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

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

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

[65] 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.

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

2006:

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

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

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

[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 [YRL-257] ).

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

2005:

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

 

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

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

 

[54] Tong Zhang and Rie K. Ando. Analysis of Spectral Kernel Design based Semi-supervised Learning. NIPS, 2005 (long version is [RC23713]).

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

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

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

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

2004:

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

 

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

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

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

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

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

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

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

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

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

2003:

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

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

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

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

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

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

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

[32] Tong Zhang and Bin Yu. On the convergence of boosting procedures. 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.

2002:

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

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

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

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

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

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

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

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

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

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

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

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

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

2001:

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

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

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

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

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

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

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

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

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

2000:

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

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

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

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

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

 

Some earlier papers:

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

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

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

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

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

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

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

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

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

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

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

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