Densities of Self-Similar Measures on the Line. - Robert S. Strichartz; Arthur Taylor; Tong Zhang 001 (Exp. Math., 1995)

Optimal Surface Smoothing as Filter Design. - Gabriel Taubin; Tong Zhang 001; Gene H. Golub (ECCV (1), 1996)

A progressive Ziv-Lempel algorithm for image compression. - Daniel Greene; Mohan Vishwanath; F. Frances Yao; Tong Zhang 001 (SEQUENCES, 1997)

Compression by Model Combination. - Tong Zhang 001 (Data Compression Conference, 1998)

A Linear Algorithm for Optimal Context Clustering with Application to Bi-level Image Coding. - Daniel H. Greene; F. Frances Yao; Tong Zhang 001 (ICIP (1), 1998)

On the Homotopy Method for Perturbed Symmetric Generalized Eigenvalue Problems. - Tong Zhang 001; Kincho H. Law; Gene H. Golub (SIAM J. Sci. Comput., 1998)

Methods for computational and statistical estimation with applications. - Tong Zhang 001 (1998)

Theoretical Analysis of a Class of Randomized Regularization Methods. - Tong Zhang 001 (COLT, 1999)

Fast, Robust, and Consistent Camera Motion Estimation. - Tong Zhang 001; Carlo Tomasi (CVPR, 1999)

Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions. - Tong Zhang 001 (NIPS, 1999)

Active learning using adaptive resampling. - Vijay S. Iyengar; Chidanand Apté; Tong Zhang 001 (KDD, 2000)

Convergence of Large Margin Separable Linear Classification. - Tong Zhang 001 (NIPS, 2000)

Regularized Winnow Methods. - Tong Zhang 001 (NIPS, 2000)

Text Chunking using Regularized Winnow. - Tong Zhang 001; Fred Damerau; David Johnson 002 (ACL, 2001)

A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning. - Tong Zhang 001 (COLT/EuroCOLT, 2001)

A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning. - Tong Zhang 001 (COLT/EuroCOLT, 2001)

Some Sparse Approximation Bounds for Regression Problems. - Tong Zhang 001 (ICML, 2001)

Generalization Performance of Some Learning Problems in Hilbert Functional Spaces. - Tong Zhang 001 (NIPS, 2001)

A General Greedy Approximation Algorithm with Applications. - Tong Zhang 001 (NIPS, 2001)

Empirical Study of Recommender Systems Using Linear Classifiers. - Vijay S. Iyengar; Tong Zhang 001 (PAKDD, 2001)

An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. - Tong Zhang 001 (AI Mag., 2001)

Text Categorization Based on Regularized Linear Classification Methods. - Tong Zhang 001; Frank J. Oles (Inf. Retr., 2001)

Rank-One Approximation to High Order Tensors. - Tong Zhang 001; Gene H. Golub (SIAM J. Matrix Anal. Appl., 2001)

The Consistency of Greedy Algorithms for Classification. - Shie Mannor; Ron Meir; Tong Zhang 001 (COLT, 2002)

Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond. - Tong Zhang 001 (ICML, 2002)

Data-Dependent Bounds for Bayesian Mixture Methods. - Ron Meir; Tong Zhang 001 (NIPS, 2002)

Effective Dimension and Generalization of Kernel Learning. - Tong Zhang 001 (NIPS, 2002)

Experiments in high-dimensional text categorization. - Fred Damerau; Tong Zhang 001; Sholom M. Weiss; Nitin Indurkhya (SIGIR, 2002)

A decision-tree-based symbolic rule induction system for text categorization. - David E. Johnson 002; Frank J. Oles; Tong Zhang 001; Thilo Götz (IBM Syst. J., 2002)

On the Consistency of Instantaneous Rigid Motion Estimation. - Tong Zhang 001; Carlo Tomasi (Int. J. Comput. Vis., 2002)

Covering Number Bounds of Certain Regularized Linear Function Classes. - Tong Zhang 001 (J. Mach. Learn. Res., 2002)

Text Chunking based on a Generalization of Winnow. - Tong Zhang 001; Fred Damerau; David Johnson 002 (J. Mach. Learn. Res., 2002)

Recommender Systems Using Linear Classifier. - Tong Zhang 001; Vijay S. Iyengar (J. Mach. Learn. Res., 2002)

On the Dual Formulation of Regularized Linear Systems with Convex Risks. - Tong Zhang 001 (Mach. Learn., 2002)

Approximation Bounds for Some Sparse Kernel Regression Algorithms. - Tong Zhang 001 (Neural Comput., 2002)

Two-Sided Arnoldi and Nonsymmetric Lanczos Algorithms. - Jane Cullum; Tong Zhang 001 (SIAM J. Matrix Anal. Appl., 2002)

Updating an NLP system to fit new domains: an empirical study on the sentence segmentation problem. - Tong Zhang 001; Fred Damerau; David Johnson 002 (CoNLL, 2003)

A Robust Risk Minimization based Named Entity Recognition System. - Tong Zhang 001; David Johnson 002 (CoNLL, 2003)

Named Entity Recognition through Classifier Combination. - Radu Florian; Abraham Ittycheriah; Hongyan Jing; Tong Zhang 001 (CoNLL, 2003)

HowtogetaChineseName(Entity): Segmentation and Combination Issues. - Hongyan Jing; Radu Florian; Xiaoqiang Luo; Tong Zhang 001; Abraham Ittycheriah (EMNLP, 2003)

On the Convergence of Boosting Procedures. - Tong Zhang 001; Bin Yu 001 (ICML, 2003)

An Infinity-sample Theory for Multi-category Large Margin Classification. - Tong Zhang 001 (NIPS, 2003)

Learning Bounds for a Generalized Family of Bayesian Posterior Distributions. - Tong Zhang 001 (NIPS, 2003)

Greedy Algorithms for Classification – Consistency, Convergence Rates, and Adaptivity. - Shie Mannor; Ron Meir; Tong Zhang 001 (J. Mach. Learn. Res., 2003)

Generalization Error Bounds for Bayesian Mixture Algorithms. - Ron Meir; Tong Zhang 001 (J. Mach. Learn. Res., 2003)

Leave-One-Out Bounds for Kernel Methods. - Tong Zhang 001 (Neural Comput., 2003)

Sequential greedy approximation for certain convex optimization problems. - Tong Zhang 001 (IEEE Trans. Inf. Theory, 2003)

On the Convergence of MDL Density Estimation. - Tong Zhang 001 (COLT, 2004)

Solving large scale linear prediction problems using stochastic gradient descent algorithms. - Tong Zhang 001 (ICML, 2004)

Chinese Named Entity Recognition Based on Multilevel Linguistic Features. - Honglei Guo; Jianmin Jiang; Gang Hu; Tong Zhang 001 (IJCNLP, 2004)

Column-generation boosting methods for mixture of kernels. - Jinbo Bi; Tong Zhang 001; Kristin P. Bennett (KDD, 2004)

Support Vector Classification with Input Data Uncertainty. - Jinbo Bi; Tong Zhang 001 (NIPS, 2004)

Class-size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification. - Tong Zhang 001 (NIPS, 2004)

Focused named entity recognition using machine learning. - Li Zhang 007; Yue Pan; Tong Zhang 001 (SIGIR, 2004)

Text categorization for a comprehensive time-dependent benchmark. - Fred Damerau; Tong Zhang 001; Sholom M. Weiss; Nitin Indurkhya (Inf. Process. Manag., 2004)

Statistical Analysis of Some Multi-Category Large Margin Classification Methods. - Tong Zhang 001 (J. Mach. Learn. Res., 2004)

A High-Performance Semi-Supervised Learning Method for Text Chunking. - Rie Kubota Ando; Tong Zhang 001 (ACL, 2005)

A Localized Prediction Model for Statistical Machine Translation. - Christoph Tillmann; Tong Zhang 001 (ACL, 2005)

Data Dependent Concentration Bounds for Sequential Prediction Algorithms. - Tong Zhang 001 (COLT, 2005)

Localized Upper and Lower Bounds for Some Estimation Problems. - Tong Zhang 001 (COLT, 2005)

Analysis of Spectral Kernel Design based Semi-supervised Learning. - Tong Zhang 001; Rie Kubota Ando (NIPS, 2005)

TREC 2005 Genomics Track Experiments at IBM Watson. - Rie Kubota Ando; Mark Dredze; Tong Zhang 001 (TREC, 2005)

A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data. - Rie Kubota Ando; Tong Zhang 001 (J. Mach. Learn. Res., 2005)

Learning Bounds for Kernel Regression Using Effective Data Dimensionality. - Tong Zhang 001 (Neural Comput., 2005)

A Discriminative Global Training Algorithm for Statistical MT. - Christoph Tillmann; Tong Zhang 001 (ACL, 2006)

Effectiveness of Meeting Outcomes in Virtual vs. Face-to-Face Teams: A Comparison Study in China. - Zixiu Guo; John D'Ambra; Tim Turner; Huiying Zhang; Tong Zhang 001 (AMCIS, 2006)

Subset Ranking Using Regression. - David Cossock; Tong Zhang 001 (COLT, 2006)

Linear prediction models with graph regularization for web-page categorization. - Tong Zhang 001; Alexandrin Popescul; Byron Dom (KDD, 2006)

Learning on Graph with Laplacian Regularization. - Rie Kubota Ando; Tong Zhang 001 (NIPS, 2006)

Information-theoretic upper and lower bounds for statistical estimation. - Tong Zhang 001 (IEEE Trans. Inf. Theory, 2006)

Margin Based Active Learning. - Maria-Florina Balcan; Andrei Z. Broder; Tong Zhang 001 (COLT, 2007)

Two-view feature generation model for semi-supervised learning. - Rie Kubota Ando; Tong Zhang 001 (ICML, 2007)

The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. - John Langford 001; Tong Zhang 001 (NIPS, 2007)

A General Boosting Method and its Application to Learning Ranking Functions for Web Search. - Zhaohui Zheng; Hongyuan Zha; Tong Zhang 001; Olivier Chapelle; Keke Chen; Gordon Sun (NIPS, 2007)

Robust classification of rare queries using web knowledge. - Andrei Z. Broder; Marcus Fontoura; Evgeniy Gabrilovich; Amruta Joshi; Vanja Josifovski; Tong Zhang 001 (SIGIR, 2007)

On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning. - Rie Johnson; Tong Zhang 001 (J. Mach. Learn. Res., 2007)

A block bigram prediction model for statistical machine translation. - Christoph Tillmann; Tong Zhang 001 (ACM Trans. Speech Lang. Process., 2007)

Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models. - Tong Zhang 001 (NIPS, 2008)

Multi-stage Convex Relaxation for Learning with Sparse Regularization. - Tong Zhang 001 (NIPS, 2008)

An Online Relevant Set Algorithm for Statistical Machine Translation. - Christoph Tillmann; Tong Zhang 001 (IEEE Trans. Speech Audio Process., 2008)

Statistical Analysis of Bayes Optimal Subset Ranking. - David Cossock; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2008)

Graph-Based Semi-Supervised Learning and Spectral Kernel Design. - Rie Johnson; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2008)

Learning nonlinear dynamic models. - John Langford 001; Ruslan Salakhutdinov; Tong Zhang 001 (ICML, 2009)

Multi-Label Prediction via Compressed Sensing. - Daniel J. Hsu; Sham M. Kakade; John Langford 001; Tong Zhang 001 (NIPS, 2009)

Nonlinear Learning using Local Coordinate Coding. - Kai Yu 001; Tong Zhang 001; Yihong Gong (NIPS, 2009)

Sparse Online Learning via Truncated Gradient. - John Langford 001; Lihong Li 001; Tong Zhang 001 (J. Mach. Learn. Res., 2009)

On the Consistency of Feature Selection using Greedy Least Squares Regression. - Tong Zhang 001 (J. Mach. Learn. Res., 2009)

Classifying search queries using the Web as a source of knowledge. - Evgeniy Gabrilovich; Andrei Z. Broder; Marcus Fontoura; Amruta Joshi; Vanja Josifovski; Lance Riedel; Tong Zhang 001 (ACM Trans. Web, 2009)

Image Classification Using Super-Vector Coding of Local Image Descriptors. - Xi Zhou; Kai Yu 001; Tong Zhang 001; Thomas S. Huang (ECCV (5), 2010)

Improved Local Coordinate Coding using Local Tangents. - Kai Yu 001; Tong Zhang 001 (ICML, 2010)

Agnostic Active Learning Without Constraints. - Alina Beygelzimer; Daniel J. Hsu; John Langford 001; Tong Zhang 001 (NIPS, 2010)

Deep Coding Network. - Yuanqing Lin; Tong Zhang 001; Shenghuo Zhu; Kai Yu 001 (NIPS, 2010)

Robust Matrix Decomposition with Outliers - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2010)

Analysis of Multi-stage Convex Relaxation for Sparse Regularization. - Tong Zhang 001 (J. Mach. Learn. Res., 2010)

A Computational Framework for Influenza Antigenic Cartography. - Zhipeng Cai; Tong Zhang 001; Xiu-Feng Wan (PLoS Comput. Biol., 2010)

Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints. - Shai Shalev-Shwartz; Nathan Srebro; Tong Zhang 001 (SIAM J. Optim., 2010)

Fundamental Statistical Techniques. - Tong Zhang 001 (Handbook of Natural Language Processing, 2010)

Fundamentals of Predictive Text Mining. - Sholom M. Weiss; Nitin Indurkhya; Tong Zhang 001 (Springer, 2010)

Spectral Methods for Learning Multivariate Latent Tree Structure. - Animashree Anandkumar; Kamalika Chaudhuri; Daniel J. Hsu; Sham M. Kakade; Le Song; Tong Zhang 001 (NIPS, 2011)

Greedy Model Averaging. - Dong Dai 004; Tong Zhang 001 (NIPS, 2011)

Learning to Search Efficiently in High Dimensions. - Zhen Li 028; Huazhong Ning; Liangliang Cao; Tong Zhang 001; Yihong Gong; Thomas S. Huang (NIPS, 2011)

Efficient Optimal Learning for Contextual Bandits. - Miroslav Dudík; Daniel J. Hsu; Satyen Kale; Nikos Karampatziakis; John Langford 001; Lev Reyzin; Tong Zhang 001 (UAI, 2011)

Dimension-free tail inequalities for sums of random matrices. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2011)

An Analysis of Random Design Linear Regression - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2011)

A tail inequality for quadratic forms of subgaussian random vectors - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2011)

Learning with Structured Sparsity. - Junzhou Huang; Tong Zhang 001; Dimitris N. Metaxas (J. Mach. Learn. Res., 2011)

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation. - Wenyuan Li 006; Chun-Chi Liu; Tong Zhang 001; Haifeng Li; Michael S. Waterman; Xianghong Jasmine Zhou (PLoS Comput. Biol., 2011)

Robust Matrix Decomposition With Sparse Corruptions. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2011)

Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations. - Tong Zhang 001 (IEEE Trans. Inf. Theory, 2011)

Sparse Recovery With Orthogonal Matching Pursuit Under RIP. - Tong Zhang 001 (IEEE Trans. Inf. Theory, 2011)

A Proximal-Gradient Homotopy Method for the L1-Regularized Least-Squares Problem. - Lin Xiao; Tong Zhang 001 (ICML, 2012)

Selective Labeling via Error Bound Minimization. - Quanquan Gu; Tong Zhang 001; Chris H. Q. Ding; Jiawei Han 001 (NIPS, 2012)

AntigenMap 3D: an online antigenic cartography resource. - J. Lamar Barnett; Jialiang Yang; Zhipeng Cai; Tong Zhang 001; Xiu-Feng Wan (Bioinform., 2012)

Deviation Optimal Learning using Greedy Q-aggregation - Dong Dai 004; Philippe Rigollet; Tong Zhang 001 (CoRR, 2012)

Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization - Shai Shalev-Shwartz; Tong Zhang 001 (CoRR, 2012)

Proximal Stochastic Dual Coordinate Ascent - Shai Shalev-Shwartz; Tong Zhang 001 (CoRR, 2012)

Analysis of a randomized approximation scheme for matrix multiplication - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2012)

A spectral algorithm for learning Hidden Markov Models. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (J. Comput. Syst. Sci., 2012)

Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes. - Ohad Shamir; Tong Zhang 001 (ICML (1), 2013)

Accelerating Stochastic Gradient Descent using Predictive Variance Reduction. - Rie Johnson; Tong Zhang 001 (NIPS, 2013)

Accelerated Mini-Batch Stochastic Dual Coordinate Ascent. - Shai Shalev-Shwartz; Tong Zhang 001 (NIPS, 2013)

High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. - Krishnakumar Balasubramanian 002; Kai Yu 001; Tong Zhang 001 (UAI, 2013)

Aggregation of Affine Estimators. - Dong Dai 004; Philippe Rigollet; Lucy Xia; Tong Zhang 001 (CoRR, 2013)

Accelerating Stochastic Alternating Direction Method of Multipliers with Adaptive Subgradient. - Peilin Zhao; Jinwei Yang; Tong Zhang 001; Ping Li 001 (CoRR, 2013)

Stochastic dual coordinate ascent methods for regularized loss. - Shai Shalev-Shwartz; Tong Zhang 001 (J. Mach. Learn. Res., 2013)

Truncated power method for sparse eigenvalue problems. - Xiao-Tong Yuan; Tong Zhang 001 (J. Mach. Learn. Res., 2013)

A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem. - Lin Xiao; Tong Zhang 001 (SIAM J. Optim., 2013)

Compressed Counting Meets Compressed Sensing. - Ping Li 001; Cun-Hui Zhang; Tong Zhang 001 (COLT, 2014)

Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. - Ohad Shamir; Nathan Srebro; Tong Zhang 001 (ICML, 2014)

A Convergence Rate Analysis for LogitBoost, MART and Their Variant. - Peng Sun 011; Tong Zhang 001; Jie Zhou 001 (ICML, 2014)

Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization. - Xiaotong Yuan; Ping Li 001; Tong Zhang 001 (ICML, 2014)

Efficient mini-batch training for stochastic optimization. - Mu Li 003; Tong Zhang 001; Yuqiang Chen; Alexander J. Smola (KDD, 2014)

Gradient boosting factorization machines. - Chen Cheng; Fen Xia; Tong Zhang 001; Irwin King; Michael R. Lyu (RecSys, 2014)

Batch-Mode Active Learning via Error Bound Minimization. - Quanquan Gu; Tong Zhang 001; Jiawei Han 001 (UAI, 2014)

Sparse Recovery with Very Sparse Compressed Counting. - Ping Li 001; Cun-Hui Zhang; Tong Zhang 001 (CoRR, 2014)

Randomized Dual Coordinate Ascent with Arbitrary Sampling. - Zheng Qu 001; Peter Richtárik; Tong Zhang 001 (CoRR, 2014)

Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion. - Shusen Wang; Tong Zhang 001; Zhihua Zhang 004 (CoRR, 2014)

Stochastic Optimization with Importance Sampling. - Peilin Zhao; Tong Zhang 001 (CoRR, 2014)

Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling. - Peilin Zhao; Tong Zhang 001 (CoRR, 2014)

Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory. - Tuo Zhao; Han Liu 001; Tong Zhang 001 (CoRR, 2014)

Random Design Analysis of Ridge Regression. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (Found. Comput. Math., 2014)

Learning Nonlinear Functions Using Regularized Greedy Forest. - Rie Johnson; Tong Zhang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

A Proximal Stochastic Gradient Method with Progressive Variance Reduction. - Lin Xiao; Tong Zhang 001 (SIAM J. Optim., 2014)

Partial Gaussian Graphical Model Estimation. - Xiao-Tong Yuan; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2014)

Adaptive Stochastic Alternating Direction Method of Multipliers. - Peilin Zhao; Jinwei Yang; Tong Zhang 001; Ping Li 001 (ICML, 2015)

Stochastic Optimization with Importance Sampling for Regularized Loss Minimization. - Peilin Zhao; Tong Zhang 001 (ICML, 2015)

Effective Use of Word Order for Text Categorization with Convolutional Neural Networks. - Rie Johnson; Tong Zhang 001 (HLT-NAACL, 2015)

Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding. - Rie Johnson; Tong Zhang 001 (NIPS, 2015)

Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling. - Zheng Qu 001; Peter Richtárik; Tong Zhang 001 (NIPS, 2015)

Local Smoothness in Variance Reduced Optimization. - Daniel Vainsencher; Han Liu 001; Tong Zhang 001 (NIPS, 2015)

Crowd Fraud Detection in Internet Advertising. - Tian Tian 001; Jun Zhu 001; Fen Xia; Xin Zhuang; Tong Zhang 001 (WWW, 2015)

Semi-Supervised Learning with Multi-View Embedding: Theory and Application with Convolutional Neural Networks. - Rie Johnson; Tong Zhang 001 (CoRR, 2015)

Towards More Efficient Nystrom Approximation and CUR Matrix Decomposition. - Shusen Wang; Zhihua Zhang 004; Tong Zhang 001 (CoRR, 2015)

Improved Analyses of the Randomized Power Method and Block Lanczos Method. - Shusen Wang; Zhihua Zhang 004; Tong Zhang 001 (CoRR, 2015)

Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference. - Zhuoran Yang; Zhaoran Wang 001; Han Liu 001; Yonina C. Eldar; Tong Zhang 001 (CoRR, 2015)

Learning sparse low-threshold linear classifiers. - Sivan Sabato; Shai Shalev-Shwartz; Nathan Srebro; Daniel J. Hsu; Tong Zhang 001 (J. Mach. Learn. Res., 2015)

Fundamentals of Predictive Text Mining, Second Edition - Sholom M. Weiss; Nitin Indurkhya; Tong Zhang 001 (Springer, 2015)

Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings. - Rie Johnson; Tong Zhang 001 (ICML, 2016)

Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity. - Zhuoran Yang; Zhaoran Wang 001; Han Liu 001; Yonina C. Eldar; Tong Zhang 001 (ICML, 2016)

Generalized Hierarchical Sparse Model for Arbitrary-Order Interactive Antigenic Sites Identification in Flu Virus Data. - Lei Han 001; Yu Zhang 006; Xiu-Feng Wan; Tong Zhang 001 (KDD, 2016)

Fast Component Pursuit for Large-Scale Inverse Covariance Estimation. - Lei Han 001; Yu Zhang 006; Tong Zhang 001 (KDD, 2016)

Exact Recovery of Hard Thresholding Pursuit. - Xiao-Tong Yuan; Ping Li 001; Tong Zhang 001 (NIPS, 2016)

Learning Additive Exponential Family Graphical Models via ell_{2, 1}-norm Regularized M-Estimation. - Xiao-Tong Yuan; Ping Li 001; Tong Zhang 001; Qingshan Liu 001; Guangcan Liu (NIPS, 2016)

Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression. - Lei Han 001; Ting Yang; Tong Zhang 001 (CoRR, 2016)

Supervised and Semi-Supervised Text Categorization using One-Hot LSTM for Region Embeddings. - Rie Johnson; Tong Zhang 001 (CoRR, 2016)

Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level. - Rie Johnson; Tong Zhang 001 (CoRR, 2016)

A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization. - Shun Zheng; Fen Xia; Wei Xu; Tong Zhang 001 (CoRR, 2016)

Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition. - Shusen Wang; Zhihua Zhang 004; Tong Zhang 001 (J. Mach. Learn. Res., 2016)

Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization. - Shai Shalev-Shwartz; Tong Zhang 001 (Math. Program., 2016)

Deep Pyramid Convolutional Neural Networks for Text Categorization. - Rie Johnson; Tong Zhang 001 (ACL (1), 2017)

Efficient Distributed Learning with Sparsity. - Jialei Wang; Mladen Kolar; Nathan Srebro; Tong Zhang 001 (ICML, 2017)

Projection-free Distributed Online Learning in Networks. - Wenpeng Zhang 003; Peilin Zhao; Wenwu Zhu 001; Steven C. H. Hoi; Tong Zhang 001 (ICML, 2017)

Diffusion Approximations for Online Principal Component Estimation and Global Convergence. - Chris Junchi Li; Mengdi Wang; Tong Zhang 001 (NIPS, 2017)

On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning. - Xingguo Li; Lin Yang 011; Jason Ge; Jarvis D. Haupt; Tong Zhang 001; Tuo Zhao (NIPS, 2017)

On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions. - Xingguo Li; Lin F. Yang; Jason Ge; Jarvis D. Haupt; Tong Zhang 001; Tuo Zhao (CoRR, 2017)

Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations. - Jialei Wang; Tong Zhang 001 (CoRR, 2017)

Candidates v.s. Noises Estimation for Large Multi-Class Classification Problem. - Lei Han 001; Tong Zhang 001 (CoRR, 2017)

Gradient Hard Thresholding Pursuit. - Xiao-Tong Yuan; Ping Li 001; Tong Zhang 001 (J. Mach. Learn. Res., 2017)

A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization. - Shun Zheng; Jialei Wang; Fen Xia; Wei Xu; Tong Zhang 001 (J. Mach. Learn. Res., 2017)

Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion. - Jun Song 004; Jun Xiao 001; Fei Wu 001; Haishan Wu; Tong Zhang 001; Zhongfei (Mark) Zhang; Wenwu Zhu 001 (IEEE Trans. Knowl. Data Eng., 2017)

Sparseness Analysis in the Pretraining of Deep Neural Networks. - Jun Li 027; Tong Zhang 001; Wei Luo 006; Jian Yang 003; Xiao-Tong Yuan; Jian Zhang 025 (IEEE Trans. Neural Networks Learn. Syst., 2017)

Translating Pro-Drop Languages With Reconstruction Models. - Longyue Wang; Zhaopeng Tu; Shuming Shi 001; Tong Zhang 001; Yvette Graham; Qun Liu 001 (AAAI, 2018)

Video Re-localization. - Yang Feng 001; Lin Ma 002; Wei Liu 005; Tong Zhang 001; Jiebo Luo (ECCV (14), 2018)

Neural Stereoscopic Image Style Transfer. - Xinyu Gong; Haozhi Huang 001; Lin Ma 002; Fumin Shen; Wei Liu 005; Tong Zhang 001 (ECCV (5), 2018)

Recurrent Fusion Network for Image Captioning. - Wenhao Jiang; Lin Ma 002; Yu-Gang Jiang; Wei Liu 005; Tong Zhang 001 (ECCV (2), 2018)

Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks. - Minjun Li; Haozhi Huang 001; Lin Ma 002; Wei Liu 005; Tong Zhang 001; Yu-Gang Jiang (ECCV (9), 2018)

Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry. - Yonggen Ling; Linchao Bao; Zequn Jie; Fengming Zhu; Ziyang Li; Shanmin Tang; Yongsheng Liu; Wei Liu 005; Tong Zhang 001 (ECCV (9), 2018)

Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition. - Yitong Wang; Dihong Gong; Zheng Zhou; Xing Ji; Hao Wang 050; Zhifeng Li 001; Wei Liu 005; Tong Zhang 001 (ECCV (15), 2018)

Super-Identity Convolutional Neural Network for Face Hallucination. - Kaipeng Zhang; Zhanpeng Zhang; Chia-Wen Cheng; Winston H. Hsu; Yu Qiao 001; Wei Liu 005; Tong Zhang 001 (ECCV (11), 2018)

Exploiting Deep Representations for Neural Machine Translation. - Zi-Yi Dou; Zhaopeng Tu; Xing Wang 007; Shuming Shi 001; Tong Zhang 001 (EMNLP, 2018)

Multi-Head Attention with Disagreement Regularization. - Jian Li 054; Zhaopeng Tu; Baosong Yang; Michael R. Lyu; Tong Zhang 001 (EMNLP, 2018)

QuaSE: Sequence Editing under Quantifiable Guidance. - Yi Liao; Lidong Bing; Piji Li; Shuming Shi 001; Wai Lam; Tong Zhang 001 (EMNLP, 2018)

Modeling Localness for Self-Attention Networks. - Baosong Yang; Zhaopeng Tu; Derek F. Wong; Fandong Meng; Lidia S. Chao; Tong Zhang 001 (EMNLP, 2018)

Candidates vs. Noises Estimation for Large Multi-Class Classification Problem. - Lei Han 001; Yiheng Huang; Tong Zhang 001 (ICML, 2018)

Composite Functional Gradient Learning of Generative Adversarial Models. - Rie Johnson; Tong Zhang 001 (ICML, 2018)

End-to-end Active Object Tracking via Reinforcement Learning. - Wenhan Luo; Peng Sun 011; Fangwei Zhong; Wei Liu 005; Tong Zhang 001; Yizhou Wang 001 (ICML, 2018)

An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method. - Li Shen 005; Peng Sun 011; Yitong Wang; Wei Liu 005; Tong Zhang 001 (ICML, 2018)

Graphical Nonconvex Optimization via an Adaptive Convex Relaxation. - Qiang Sun; Kean Ming Tan; Han Liu 001; Tong Zhang 001 (ICML, 2018)

Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization. - Jiaxiang Wu 001; Weidong Huang 005; Junzhou Huang; Tong Zhang 001 (ICML, 2018)

Safe Element Screening for Submodular Function Minimization. - Weizhong Zhang; Bin Hong; Lin Ma 002; Wei Liu 005; Tong Zhang 001 (ICML, 2018)

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. - Kaiqing Zhang; Zhuoran Yang; Han Liu 001; Tong Zhang 001; Tamer Basar (ICML, 2018)

Sketched Follow-The-Regularized-Leader for Online Factorization Machine. - Luo Luo; Wenpeng Zhang 003; Zhihua Zhang 004; Wenwu Zhu 001; Tong Zhang 001; Jian Pei (KDD, 2018)

Stochastic Expectation Maximization with Variance Reduction. - Jianfei Chen 001; Jun Zhu 001; Yee Whye Teh; Tong Zhang 001 (NeurIPS, 2018)

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. - Cong Fang 001; Chris Junchi Li; Zhouchen Lin; Tong Zhang 001 (NeurIPS, 2018)

Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity. - Conghui Tan; Tong Zhang 001; Shiqian Ma; Ji Liu (NeurIPS, 2018)

Communication Compression for Decentralized Training. - Hanlin Tang; Shaoduo Gan; Ce Zhang 001; Tong Zhang 001; Ji Liu 002 (NeurIPS, 2018)

Exponentially Weighted Imitation Learning for Batched Historical Data. - Qing Wang; Jiechao Xiong; Lei Han 001; Peng Sun 011; Han Liu 001; Tong Zhang 001 (NeurIPS, 2018)

Gradient Sparsification for Communication-Efficient Distributed Optimization. - Jianqiao Wangni; Jialei Wang; Ji Liu; Tong Zhang 001 (NeurIPS, 2018)

Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset. - Xinpeng Chen; Jingyuan Chen; Lin Ma 002; Jian Yao 002; Wei Liu 005; Jiebo Luo; Tong Zhang 001 (WWW (Companion Volume), 2018)

Decentralization Meets Quantization. - Hanlin Tang; Ce Zhang 001; Shaoduo Gan; Tong Zhang 001; Ji Liu 002 (CoRR, 2018)

Incorporating Pseudo-Parallel Data for Quantifiable Sequence Editing. - Yi Liao; Lidong Bing; Piji Li; Shuming Shi 001; Wai Lam; Tong Zhang 001 (CoRR, 2018)

Diffusion Approximations for Online Principal Component Estimation and Global Convergence. - Chris Junchi Li; Mengdi Wang; Han Liu 001; Tong Zhang 001 (CoRR, 2018)

A convex formulation for high-dimensional sparse sliced inverse regression. - Kean Ming Tan; Zhaoran Wang 001; Tong Zhang 001; Han Liu 001; R. Dennis Cook (CoRR, 2018)

TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game. - Peng Sun 011; Xinghai Sun; Lei Han 001; Jiechao Xiong; Qing Wang; Bo Li 064; Yang Zheng; Ji Liu 001; Yongsheng Liu; Han Liu 001; Tong Zhang 001 (CoRR, 2018)

Fully Implicit Online Learning. - Chao-Bing Song; Ji Liu 001; Han Liu 001; Yong Jiang 001; Tong Zhang 001 (CoRR, 2018)

Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space. - Jiechao Xiong; Qing Wang; Zhuoran Yang; Peng Sun 011; Lei Han 001; Yang Zheng; Haobo Fu; Tong Zhang 001; Ji Liu 001; Han Liu 001 (CoRR, 2018)

Finite-Sample Analyses for Fully Decentralized Multi-Agent Reinforcement Learning. - Kaiqing Zhang; Zhuoran Yang; Han Liu 001; Tong Zhang 001; Tamer Basar (CoRR, 2018)

Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. - Haishan Ye; Zhichao Huang; Cong Fang 001; Chris Junchi Li; Tong Zhang 001 (CoRR, 2018)

An Ensemble Approach for Detecting Anomalous User Behaviors. - Xiangyu Xi; Tong Zhang 001; Wei Ye 004; Wen Zhao; Shikun Zhang; Dongdong Du; Qing Gao (Int. J. Softw. Eng. Knowl. Eng., 2018)

Near-optimal stochastic approximation for online principal component estimation. - Chris Junchi Li; Mengdi Wang; Han Liu 001; Tong Zhang 001 (Math. Program., 2018)

Learning to Remember Translation History with a Continuous Cache. - Zhaopeng Tu; Yang Liu 005; Shuming Shi 001; Tong Zhang 001 (Trans. Assoc. Comput. Linguistics, 2018)

Bayesian Model Averaging With Exponentiated Least Squares Loss. - Dong Dai 004; Lei Han 001; Ting Yang; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2018)

Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement. - Zi-Yi Dou; Zhaopeng Tu; Xing Wang 007; Longyue Wang; Shuming Shi 001; Tong Zhang 001 (AAAI, 2019)

Neural Machine Translation with Adequacy-Oriented Learning. - Xiang Kong; Zhaopeng Tu; Shuming Shi 001; Eduard H. Hovy; Tong Zhang 001 (AAAI, 2019)

Reinforced Training Data Selection for Domain Adaptation. - Miaofeng Liu; Yan Song 003; Hongbin Zou; Tong Zhang 001 (ACL (1), 2019)

Sentiment Analysis Using Autoregressive Language Modeling and Broad Learning System. - Xin-Rong Gong; Jian-Xiu Jin; Tong Zhang 001 (BIBM, 2019)

Sharp Analysis for Nonconvex SGD Escaping from Saddle Points. - Cong Fang 001; Zhouchen Lin; Tong Zhang 001 (COLT, 2019)

Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition. - Yinpeng Dong; Hang Su 006; Baoyuan Wu; Zhifeng Li 001; Wei Liu 005; Tong Zhang 001; Jun Zhu 001 (CVPR, 2019)

DHER: Hindsight Experience Replay for Dynamic Goals. - Meng Fang; Cheng Zhou; Bei Shi; Boqing Gong; Jia Xu 011; Tong Zhang 001 (ICLR (Poster), 2019)

Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI. - Lei Han 001; Peng Sun 011; Yali Du 001; Jiechao Xiong; Qing Wang; Xinghai Sun; Han Liu 001; Tong Zhang 001 (ICML, 2019)

NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks. - Yandong Li; Lijun Li; Liqiang Wang; Tong Zhang 001; Boqing Gong (ICML, 2019)

DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression. - Hanlin Tang; Chen Yu 009; Xiangru Lian; Tong Zhang 001; Ji Liu 002 (ICML, 2019)

A Hybrid Character Representation for Chinese Event Detection. - Xiangyu Xi; Tong Zhang 001; Wei Ye 004; Jinglei Zhang; Rui Xie 003; Shikun Zhang (IJCNN, 2019)

Divergence-Augmented Policy Optimization. - Qing Wang; Yingru Li; Jiechao Xiong; Tong Zhang 001 (NeurIPS, 2019)

Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning. - Baoyuan Wu; Weidong Chen; Yanbo Fan; Yong Zhang 034; Jinlong Hou; Jie Liu 036; Tong Zhang 001 (IEEE Access, 2019)

Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus. - Lei Han 001; Lei Li; Feng Wen; Lei Zhong; Tong Zhang 001; Xiu-Feng Wan (Bioinform., 2019)

Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning. - Baoyuan Wu; Weidong Chen; Yanbo Fan; Yong Zhang 034; Jinlong Hou; Jie Liu 036; Junzhou Huang; Wei Liu 005; Tong Zhang 001 (CoRR, 2019)

MAP Inference via L2-Sphere Linear Program Reformulation. - Baoyuan Wu; Li Shen 008; Bernard Ghanem; Tong Zhang 001 (CoRR, 2019)

DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression. - Hanlin Tang; Xiangru Lian; Tong Zhang 001; Ji Liu 002 (CoRR, 2019)

DeepSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression. - Hanlin Tang; Xiangru Lian; Shuang Qiu; Lei Yuan 001; Ce Zhang 001; Tong Zhang 001; Ji Liu 002 (CoRR, 2019)

Mirror Natural Evolution Strategies. - Haishan Ye; Tong Zhang 001 (CoRR, 2019)

Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations. - Cong Fang 001; Hanze Dong; Tong Zhang 001 (CoRR, 2019)

Multi-objective Neural Architecture Search via Predictive Network Performance Optimization. - Han Shi; Renjie Pi; Hang Xu; Zhenguo Li; James T. Kwok; Tong Zhang 001 (CoRR, 2019)

Fast Generalized Matrix Regression with Applications in Machine Learning. - Haishan Ye; Shusen Wang; Zhihua Zhang 004; Tong Zhang 001 (CoRR, 2019)

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python. - Jason Ge; Xingguo Li; Haoming Jiang; Han Liu 001; Tong Zhang 001; Mengdi Wang; Tuo Zhao (J. Mach. Learn. Res., 2019)

Robust Frequent Directions with Application in Online Learning. - Luo Luo; Cheng Chen 015; Zhihua Zhang 004; Wu-Jun Li; Tong Zhang 001 (J. Mach. Learn. Res., 2019)

Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models. - Kean Ming Tan; Junwei Lu; Tong Zhang 001; Han Liu 001 (J. Mach. Learn. Res., 2019)

Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations. - Jialei Wang; Tong Zhang 001 (J. Mach. Learn. Res., 2019)

Stable Learning via Sample Reweighting. - Zheyan Shen; Peng Cui 001; Tong Zhang 001; Kun Kuang (AAAI, 2020)

Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge. - Yuanhe Tian; Yan Song 003; Xiang Ao 001; Fei Xia; Xiaojun Quan; Tong Zhang 001; Yonggang Wang (ACL, 2020)

Improving Chinese Word Segmentation with Wordhood Memory Networks. - Yuanhe Tian; Yan Song 003; Fei Xia; Tong Zhang 001; Yonggang Wang (ACL, 2020)

Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data. - Qi Chang; Hui Qu; Yikai Zhang 003; Mert R. Sabuncu; Chao Chen 012; Tong Zhang 001; Dimitris N. Metaxas (CVPR, 2020)

MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. - Chaoyang He 001; Haishan Ye; Li Shen 005; Tong Zhang 001 (CVPR, 2020)

Leveraging Human Prior Knowledge to Learn Sense Representations. - Tong Zhang 001; Wei Ye 004; Xiangyu Xi; Long Zhang; Shikun Zhang; Wen Zhao (ECAI, 2020)

CATCH: Context-Based Meta Reinforcement Learning for Transferrable Architecture Search. - Xin Chen; Yawen Duan; Zewei Chen; Hang Xu; Zihao Chen; Xiaodan Liang; Tong Zhang 001; Zhenguo Li (ECCV (19), 2020)

ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations. - Shizhe Diao; Jiaxin Bai; Yan Song 003; Tong Zhang 001; Yonggang Wang (EMNLP (Findings), 2020)

Improving Constituency Parsing with Span Attention. - Yuanhe Tian; Yan Song 003; Fei Xia; Tong Zhang 001 (EMNLP (Findings), 2020)

Black-Box Adversarial Attack with Transferable Model-based Embedding. - Zhichao Huang; Tong Zhang 001 (ICLR, 2020)

Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization. - Rie Johnson; Tong Zhang 001 (ICML, 2020)

Stable Learning via Differentiated Variable Decorrelation. - Zheyan Shen; Peng Cui 001; Jiashuo Liu; Tong Zhang 001; Bo Li 064; Zhitang Chen (KDD, 2020)

A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. - Zixiang Chen; Yuan Cao 006; Quanquan Gu; Tong Zhang 001 (NeurIPS, 2020)

How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. - Yihong Gu; Weizhong Zhang; Cong Fang 001; Jason D. Lee; Tong Zhang 001 (NeurIPS, 2020)

Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets. - Kai Han 002; Yunhe Wang 001; Qiulin Zhang; Wei Zhang 196; Chunjing Xu; Tong Zhang 001 (NeurIPS, 2020)

Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts. - Guilin Li; Junlei Zhang; Yunhe Wang 001; Chuanjian Liu; Matthias Tan; Yunfeng Lin; Wei Zhang 196; Jiashi Feng; Tong Zhang 001 (NeurIPS, 2020)

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. - Luo Luo; Haishan Ye; Zhichao Huang; Tong Zhang 001 (NeurIPS, 2020)

Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS. - Han Shi; Renjie Pi; Hang Xu; Zhenguo Li; James T. Kwok; Tong Zhang 001 (NeurIPS, 2020)

Decentralized Accelerated Proximal Gradient Descent. - Haishan Ye; Ziang Zhou; Luo Luo; Tong Zhang 001 (NeurIPS, 2020)

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. - Luo Luo; Haishan Ye; Tong Zhang 001 (CoRR, 2020)

Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. - Zixiang Chen; Yuan Cao 006; Quanquan Gu; Tong Zhang 001 (CoRR, 2020)

Bidirectional Generative Modeling Using Adversarial Gradient Estimation. - Xinwei Shen 002; Tong Zhang 001; Kani Chen (CoRR, 2020)

CorrAttack: Black-box Adversarial Attack with Structured Search. - Zhichao Huang; Yaowei Huang; Tong Zhang 001 (CoRR, 2020)

Disentangled Generative Causal Representation Learning. - Xinwei Shen 002; Furui Liu; Hanze Dong; Qing Lian; Zhitang Chen; Tong Zhang 001 (CoRR, 2020)

Propagation Model Search for Graph Neural Networks. - Yuhui Ding; Quanming Yao; Tong Zhang 001 (CoRR, 2020)

VEGA: Towards an End-to-End Configurable AutoML Pipeline. - Bochao Wang; Hang Xu; Jiajin Zhang; Chen Chen 077; Yixing Xu; Xiaozhi Fang; Ning Kang 001; Lanqing Hong; Chenhan Jiang; Xinyue Cai; Jiawei Li; Fengwei Zhou; et al. (CoRR, 2020)

Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information. - Qi Chang; Zhennan Yan; Lohendran Baskaran; Hui Qu; Yikai Zhang 003; Tong Zhang 001; Shaoting Zhang 001; Dimitris N. Metaxas (CoRR, 2020)

PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction. - Haishan Ye; Wei Xiong 015; Tong Zhang 001 (CoRR, 2020)

MAP Inference Via ℓ 2-Sphere Linear Program Reformulation. - Baoyuan Wu; Li Shen 008; Tong Zhang 001; Bernard Ghanem (Int. J. Comput. Vis., 2020)

Accelerated dual-averaging primal-dual method for composite convex minimization. - Conghui Tan; Yuqiu Qian; Shiqian Ma; Tong Zhang 001 (Optim. Methods Softw., 2020)

End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning. - Wenhan Luo; Peng Sun 011; Fangwei Zhong; Wei Liu 005; Tong Zhang 001; Yizhou Wang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold. - Shixiang Chen; Shiqian Ma; Anthony Man-Cho So; Tong Zhang 001 (SIAM J. Optim., 2020)

TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation. - Shizhe Diao; Xinwei Shen 002; Kashun Shum; Yan Song 003; Tong Zhang 001 (ACL/IJCNLP (Findings), 2021)

Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation. - Shizhe Diao; Ruijia Xu; Hongjin Su; Yilei Jiang; Yan Song 003; Tong Zhang 001 (ACL/IJCNLP (1), 2021)

Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation. - Tong Zhang 001; Long Zhang; Wei Ye 004; Bo Li 099; Jinan Sun; Xiaoyu Zhu; Wen Zhao; Shikun Zhang (ACL/IJCNLP (1), 2021)

Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. - Cong Fang 001; Jason D. Lee; Pengkun Yang; Tong Zhang 001 (COLT, 2021)

TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search. - Yawen Duan; Xin Chen; Hang Xu; Zewei Chen; Xiaodan Liang; Tong Zhang 001; Zhenguo Li (CVPR, 2021)

Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling. - Zhichao Huang; Xintong Han; Jia Xu 011; Tong Zhang 001 (CVPR, 2021)

Involution: Inverting the Inherence of Convolution for Visual Recognition. - Duo Li; Jie Hu 019; Changhu Wang; Xiangtai Li; Qi She; Lei Zhu 012; Tong Zhang 001; Qifeng Chen (CVPR, 2021)

Joint-DetNAS: Upgrade Your Detector With NAS, Pruning and Dynamic Distillation. - Lewei Yao; Renjie Pi; Hang Xu; Wei Zhang 196; Zhenguo Li; Tong Zhang 001 (CVPR, 2021)

Effective Sparsification of Neural Networks With Global Sparsity Constraint. - Xiao Zhou; Weizhong Zhang; Hang Xu; Tong Zhang 001 (CVPR, 2021)

Improving Event Detection by Exploiting Label Hierarchy. - Xiangyu Xi; Wei Ye 004; Tong Zhang 001; Quanxiu Wang; Shikun Zhang; Huixing Jiang; Wei Wu 014 (ICASSP, 2021)

G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation. - Lewei Yao; Renjie Pi; Hang Xu; Wei Zhang 196; Zhenguo Li; Tong Zhang 001 (ICCV, 2021)

DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks. - Yuhui Ding; Quanming Yao; Huan Zhao 002; Tong Zhang 001 (KDD, 2021)

Multi-Hop Transformer for Document-Level Machine Translation. - Long Zhang; Tong Zhang 001; Haibo Zhang 013; Baosong Yang; Wei Ye 004; Shikun Zhang (NAACL-HLT, 2021)

A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. - Christoph Dann; Mehryar Mohri; Tong Zhang 001; Julian Zimmert (NeurIPS, 2021)

Error Compensated Distributed SGD Can Be Accelerated. - Xun Qian; Peter Richtárik; Tong Zhang 001 (NeurIPS, 2021)

Efficient Neural Network Training via Forward and Backward Propagation Sparsification. - Xiao Zhou; Weizhong Zhang; Zonghao Chen; Shizhe Diao; Tong Zhang 001 (NeurIPS, 2021)

Geometry-aware data augmentation for monocular 3D object detection. - Qing Lian; Botao Ye; Ruijia Xu; Weilong Yao; Tong Zhang 001 (CoRR, 2021)

ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders. - Yan Song 003; Tong Zhang 001; Yonggang Wang; Kai-Fu Lee (CoRR, 2021)

Adder Neural Networks. - Hanting Chen; Yunhe Wang 001; Chang Xu 002; Chao Xu 006; Chunjing Xu; Tong Zhang 001 (CoRR, 2021)

Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization. - Luo Luo; Guangzeng Xie; Tong Zhang 001; Zhihua Zhang 004 (CoRR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Why Stable Learning Works? A Theory of Covariate Shift Generalization. - Renzhe Xu; Peng Cui 001; Zheyan Shen; Xingxuan Zhang; Tong Zhang 001 (CoRR, 2021)

DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. - Haishan Ye; Tong Zhang 001 (J. Mach. Learn. Res., 2021)

A Framework of Composite Functional Gradient Methods for Generative Adversarial Models. - Rie Johnson; Tong Zhang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Mathematical Models of Overparameterized Neural Networks. - Cong Fang 001; Hanze Dong; Tong Zhang 001 (Proc. IEEE, 2021)

Finite-Sample Analysis for Decentralized Batch Multiagent Reinforcement Learning With Networked Agents. - Kaiqing Zhang; Zhuoran Yang; Han Liu 001; Tong Zhang 001; Tamer Basar (IEEE Trans. Autom. Control., 2021)

Local-Global Memory Neural Network for Medication Prediction. - Jun Song 004; Yueyang Wang; Siliang Tang; Yin Zhang 006; Zhigang Chen 003; Zhongfei Zhang; Tong Zhang 001; Fei Wu 001 (IEEE Trans. Neural Networks Learn. Syst., 2021)

Frequency-Aware Contrastive Learning for Neural Machine Translation. - Tong Zhang 001; Wei Ye 004; Baosong Yang; Long Zhang; Xingzhang Ren; Dayiheng Liu; Jinan Sun; Shikun Zhang; Haibo Zhang 013; Wen Zhao (AAAI, 2022)

Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting. - Ying Su; Hongming Zhang 009; Yangqiu Song; Tong Zhang 001 (ACL (1), 2022)

Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs. - Zile Qiao; Wei Ye 004; Tong Zhang 001; Tong Mo; Weiping Li; Shikun Zhang (COLING, 2022)

Multilingual Word Sense Disambiguation with Unified Sense Representation. - Ying Su; Hongming Zhang 009; Yangqiu Song; Tong Zhang 001 (COLING, 2022)

Minimax Regret Optimization for Robust Machine Learning under Distribution Shift. - Alekh Agarwal; Tong Zhang 001 (COLT, 2022)

Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling. - Alekh Agarwal; Tong Zhang 001 (COLT, 2022)

Exploring Geometric Consistency for Monocular 3D Object Detection. - Qing Lian; Botao Ye; Ruijia Xu; Weilong Yao; Tong Zhang 001 (CVPR, 2022)

Bayesian Invariant Risk Minimization. - Yong Lin; Hanze Dong; Hao Wang 014; Tong Zhang 001 (CVPR, 2022)

Semi-supervised Monocular 3D Object Detection by Multi-view Consistency. - Qing Lian; Yanbo Xu; Weilong Yao; Yingcong Chen; Tong Zhang 001 (ECCV (8), 2022)

MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation. - Ying Su; Zihao Wang 001; Tianqing Fang; Hongming Zhang 009; Yangqiu Song; Tong Zhang 001 (EMNLP (Findings), 2022)

HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning. - Ziniu Li; Yingru Li; Yushun Zhang; Tong Zhang 001; Zhi-Quan Luo (ICLR, 2022)

Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. - Rui Pan; Haishan Ye; Tong Zhang 001 (ICLR, 2022)

Achieving Minimax Rates in Pool-Based Batch Active Learning. - Claudio Gentile; Zhilei Wang; Tong Zhang 001 (ICML, 2022)

Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. - Hao Liu 028; Minshuo Chen; Siawpeng Er; Wenjing Liao; Tong Zhang 001; Tuo Zhao (ICML, 2022)

A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. - Wei Xiong 015; Han Zhong 001; Chengshuai Shi; Cong Shen 001; Tong Zhang 001 (ICML, 2022)

A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization. - Renzhe Xu; Xingxuan Zhang; Zheyan Shen; Tong Zhang 001; Peng Cui 001 (ICML, 2022)

Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. - Han Zhong 001; Wei Xiong 015; Jiyuan Tan; Liwei Wang 001; Tong Zhang 001; Zhaoran Wang 001; Zhuoran Yang (ICML, 2022)

Model Agnostic Sample Reweighting for Out-of-Distribution Learning. - Xiao Zhou; Yong Lin; Renjie Pi; Weizhong Zhang; Renzhe Xu; Peng Cui 001; Tong Zhang 001 (ICML, 2022)

Sparse Invariant Risk Minimization. - Xiao Zhou; Yong Lin; Weizhong Zhang; Tong Zhang 001 (ICML, 2022)

Probabilistic Bilevel Coreset Selection. - Xiao Zhou; Renjie Pi; Weizhong Zhang; Yong Lin; Zonghao Chen; Tong Zhang 001 (ICML, 2022)

Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity. - Alekh Agarwal; Tong Zhang 001 (NeurIPS, 2022)

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. - Jiafan He; Dongruo Zhou; Tong Zhang 001; Quanquan Gu (NeurIPS, 2022)

Black-box Prompt Learning for Pre-trained Language Models. - Shizhe Diao; Xuechun Li; Yong Lin; Zhichao Huang; Tong Zhang 001 (CoRR, 2022)

Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization. - Yi-An Ma; Teodor Vanislavov Marinov; Tong Zhang 001 (CoRR, 2022)

Asymptotic Statistical Analysis of f-divergence GAN. - Xinwei Shen 002; Kani Chen; Tong Zhang 001 (CoRR, 2022)

GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond. - Han Zhong 001; Wei Xiong 015; Sirui Zheng; Liwei Wang 001; Zhaoran Wang 001; Zhuoran Yang; Tong Zhang 001 (CoRR, 2022)

Normalizing Flow with Variational Latent Representation. - Hanze Dong; Shizhe Diao; Weizhong Zhang; Tong Zhang 001 (CoRR, 2022)

ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT. - Rui Pan; Shizhe Diao; Jianlin Chen; Tong Zhang 001 (CoRR, 2022)

Weakly Supervised Disentangled Generative Causal Representation Learning. - Xinwei Shen 002; Furui Liu; Hanze Dong; Qing Lian; Zhitang Chen; Tong Zhang 001 (J. Mach. Learn. Res., 2022)

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint. - Yoav Freund; Yi-An Ma; Tong Zhang 001 (J. Mach. Learn. Res., 2022)

A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization. - Minghan Yang; Andre Milzarek; Zaiwen Wen; Tong Zhang 001 (Math. Program., 2022)

Convex Formulation of Overparameterized Deep Neural Networks. - Cong Fang 001; Yihong Gu; Weizhong Zhang; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2022)

Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning. - Tong Zhang 001 (SIAM J. Math. Data Sci., 2022)

Covariate-Shift Generalization via Random Sample Weighting. - Yue He 001; Xinwei Shen 002; Renzhe Xu; Tong Zhang 001; Yong Jiang 001; Wenchao Zou; Peng Cui 001 (AAAI, 2023)

Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models' Memories. - Shizhe Diao; Tianyang Xu; Ruijia Xu; Jiawei Wang; Tong Zhang 001 (ACL (1), 2023)

Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. - Xun Qian; Hanze Dong; Tong Zhang 001; Peter Richtárik (AISTATS, 2023)

VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. - Alekh Agarwal; Yujia Jin; Tong Zhang 001 (COLT, 2023)

Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. - Heyang Zhao; Jiafan He; Dongruo Zhou; Tong Zhang 001; Quanquan Gu (COLT, 2023)

Doolittle: Benchmarks and Corpora for Academic Writing Formalization. - Shizhe Diao; Yongyu Lei; Liangming Pan; Tianqing Fang; Wangchunshu Zhou; Sedrick Scott Keh; Min-Yen Kan; Tong Zhang 001 (EMNLP, 2023)

DetGPT: Detect What You Need via Reasoning. - Renjie Pi; Jiahui Gao; Shizhe Diao; Rui Pan; Hanze Dong; Jipeng Zhang; Lewei Yao; Jianhua Han; Hang Xu; Lingpeng Kong; Tong Zhang 001 (EMNLP, 2023)

Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data. - Kashun Shum; Shizhe Diao; Tong Zhang 001 (EMNLP (Findings), 2023)

Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. - Wei Xiong 015; Han Zhong 001; Chengshuai Shi; Cong Shen 001; Liwei Wang 001; Tong Zhang 001 (ICLR, 2023)

Particle-based Variational Inference with Preconditioned Functional Gradient Flow. - Hanze Dong; Xi Wang; Yong Lin; Tong Zhang 001 (ICLR, 2023)

Learning in POMDPs is Sample-Efficient with Hindsight Observability. - Jonathan Lee 002; Alekh Agarwal; Christoph Dann; Tong Zhang 001 (ICML, 2023)

On the Convergence of Federated Averaging with Cyclic Client Participation. - Yae Jee Cho; Pranay Sharma; Gauri Joshi; Zheng Xu 002; Satyen Kale; Tong Zhang 001 (ICML, 2023)

Beyond Uniform Lipschitz Condition in Differentially Private Optimization. - Rudrajit Das; Satyen Kale; Zheng Xu 002; Tong Zhang 001; Sujay Sanghavi (ICML, 2023)

Generalized Polyak Step Size for First Order Optimization with Momentum. - Xiaoyu Wang 008; Mikael Johansson 001; Tong Zhang 001 (ICML, 2023)

What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL? - Rui Yang 010; Lin Yong; Xiaoteng Ma; Hao Hu 006; Chongjie Zhang; Tong Zhang 001 (ICML, 2023)

Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. - Chenlu Ye; Wei Xiong 015; Quanquan Gu; Tong Zhang 001 (ICML, 2023)

ZipKV: In-Memory Key-Value Store with Built-In Data Compression. - Linsen Ma; Rui Xie 003; Tong Zhang 001 (ISMM, 2023)

A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes. - Han Zhong 001; Tong Zhang 001 (NeurIPS, 2023)

Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage. - Jose H. Blanchet; Miao Lu; Tong Zhang 001; Han Zhong 001 (NeurIPS, 2023)

Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training. - Rie Johnson; Tong Zhang 001 (NeurIPS, 2023)

Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee. - Yuanshi Liu; Cong Fang 001; Tong Zhang 001 (NeurIPS, 2023)

Posterior Sampling for Competitive RL: Function Approximation and Partial Observation. - Shuang Qiu; Ziyu Dai; Han Zhong 001; Zhaoran Wang 001; Zhuoran Yang; Tong Zhang 001 (NeurIPS, 2023)

Corruption-Robust Offline Reinforcement Learning with General Function Approximation. - Chenlu Ye; Rui Yang 010; Quanquan Gu; Tong Zhang 001 (NeurIPS, 2023)

Hashtag-Guided Low-Resource Tweet Classification. - Shizhe Diao; Sedrick Scott Keh; Liangming Pan; Zhiliang Tian; Yan Song 003; Tong Zhang 001 (WWW, 2023)

Probabilistic Bilevel Coreset Selection. - Xiao Zhou; Renjie Pi; Weizhong Zhang; Yong Lin; Tong Zhang 001 (CoRR, 2023)

Active Prompting with Chain-of-Thought for Large Language Models. - Shizhe Diao; Pengcheng Wang; Yong Lin; Tong Zhang 001 (CoRR, 2023)

Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium. - Shihong Ding; Hanze Dong; Cong Fang 001; Zhouchen Lin; Tong Zhang 001 (CoRR, 2023)

Environment Invariant Linear Least Squares. - Jianqing Fan; Cong Fang 001; Yihong Gu; Tong Zhang 001 (CoRR, 2023)

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. - Hanze Dong; Wei Xiong 015; Deepanshu Goyal; Rui Pan; Shizhe Diao; Jipeng Zhang; Kashun Shum; Tong Zhang 001 (CoRR, 2023)

What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL? - Rui Yang 010; Yong Lin; Xiaoteng Ma; Hao Hu 006; Chongjie Zhang; Tong Zhang 001 (CoRR, 2023)

Monte Carlo Sampling without Isoperimetry: A Reverse Diffusion Approach. - Xunpeng Huang; Hanze Dong; Yifan Hao; Yian Ma; Tong Zhang 001 (CoRR, 2023)

Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning. - Yong Lin; Chen Liu; Chenlu Ye; Qing Lian; Yuan Yao; Tong Zhang 001 (CoRR, 2023)

Mitigating the Alignment Tax of RLHF. - Yong Lin; Hangyu Lin; Wei Xiong 015; Shizhe Diao; Jianmeng Liu; Jipeng Zhang; Rui Pan; Haoxiang Wang 003; Wenbin Hu 002; Hanning Zhang; Hanze Dong; Renjie Pi; et al. (CoRR, 2023)

MEDL-U: Uncertainty-aware 3D Automatic Annotator based on Evidential Deep Learning. - Helbert Paat; Qing Lian; Weilong Yao; Tong Zhang 001 (CoRR, 2023)

PerceptionGPT: Effectively Fusing Visual Perception into LLM. - Renjie Pi; Lewei Yao; Jiahui Gao; Jipeng Zhang; Tong Zhang 001 (CoRR, 2023)

Plum: Prompt Learning using Metaheuristic. - Rui Pan; Shuo Xing; Shizhe Diao; Xiang Liu; Kashun Shum; Jipeng Zhang; Tong Zhang 001 (CoRR, 2023)

R-Tuning: Teaching Large Language Models to Refuse Unknown Questions. - Hanning Zhang; Shizhe Diao; Yong Lin; Yi R. Fung 001; Qing Lian; Xingyao Wang 002; Yangyi Chen; Heng Ji; Tong Zhang 001 (CoRR, 2023)

An Incremental Unified Framework for Small Defect Inspection. - Jiaqi Tang 005; Hao Lu 009; Xiaogang Xu; Ruizheng Wu; Sixing Hu; Tong Zhang 001; Tsz Wa Cheng; Ming Ge; Ying-Cong Chen; Fugee Tsung (CoRR, 2023)

Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF. - Wei Xiong 015; Hanze Dong; Chenlu Ye; Han Zhong 001; Nan Jiang 008; Tong Zhang 001 (CoRR, 2023)

Multi-Consensus Decentralized Accelerated Gradient Descent. - Haishan Ye; Luo Luo; Ziang Zhou; Tong Zhang 001 (J. Mach. Learn. Res., 2023)

Black-Box Prompt Learning for Pre-trained Language Models. - Shizhe Diao; Zhichao Huang; Ruijia Xu; Xuechun Li; Yong Lin; Xiao Zhou; Tong Zhang 001 (Trans. Mach. Learn. Res., 2023)

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. - Hanze Dong; Wei Xiong 015; Deepanshu Goyal; Yihan Zhang; Winnie Chow; Rui Pan; Shizhe Diao; Jipeng Zhang; Kashun Shum; Tong Zhang 001 (Trans. Mach. Learn. Res., 2023)

Active Prompting with Chain-of-Thought for Large Language Models. - Shizhe Diao; Pengcheng Wang; Yong Lin; Rui Pan; Xiang Liu; Tong Zhang 001 (ACL (1), 2024)

Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation. - Cheng Niu; Xingguang Wang; Xuxin Cheng; Juntong Song; Tong Zhang 001 (ACL (1), 2024)

RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models. - Cheng Niu; Yuanhao Wu; Juno Zhu; Siliang Xu; Kashun Shum; Randy Zhong; Juntong Song; Tong Zhang 001 (ACL (1), 2024)

Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards. - Haoxiang Wang 003; Yong Lin; Wei Xiong 015; Rui Yang 010; Shizhe Diao; Shuang Qiu; Han Zhao 002; Tong Zhang 001 (ACL (1), 2024)

Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. - Xunpeng Huang; Difan Zou; Hanze Dong; Yi-An Ma; Tong Zhang 001 (COLT, 2024)

Strengthening Multimodal Large Language Model with Bootstrapped Preference Optimization. - Renjie Pi; Tianyang Han; Wei Xiong 015; Jipeng Zhang; Runtao Liu; Rui Pan; Tong Zhang 001 (ECCV (33), 2024)

Towards Robust Offline Reinforcement Learning under Diverse Data Corruption. - Rui Yang 010; Han Zhong 001; Jiawei Xu; Amy Zhang; Chongjie Zhang; Lei Han 001; Tong Zhang 001 (ICLR, 2024)

Reverse Diffusion Monte Carlo. - Xunpeng Huang; Hanze Dong; Yifan Hao; Yian Ma; Tong Zhang 001 (ICLR, 2024)

Spurious Feature Diversification Improves Out-of-distribution Generalization. - Yong Lin; Lu Tan; Yifan Hao; Honam Wong; Hanze Dong; Weizhong Zhang; Yujiu Yang; Tong Zhang 001 (ICLR, 2024)

Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise. - Rui Pan; Yuxing Liu; Xiaoyu Wang 008; Tong Zhang 001 (ICLR, 2024)

Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint. - Wei Xiong 015; Hanze Dong; Chenlu Ye; Ziqi Wang; Han Zhong 001; Heng Ji; Nan Jiang 008; Tong Zhang 001 (ICML, 2024)

The Non-linear F-Design and Applications to Interactive Learning. - Alekh Agarwal; Jian Qian; Alexander Rakhlin; Tong Zhang 001 (ICML, 2024)

Faster Sampling via Stochastic Gradient Proximal Sampler. - Xunpeng Huang; Difan Zou; Hanze Dong; Yian Ma; Tong Zhang 001 (ICML, 2024)

Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. - Chenlu Ye; Jiafan He; Quanquan Gu; Tong Zhang 001 (ICML, 2024)

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning. - Dake Zhang; Boxiang Lyu; Shuang Qiu; Mladen Kolar; Tong Zhang 001 (ICML, 2024)

MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning. - Helbert Paat; Qing Lian; Weilong Yao; Tong Zhang 001 (ICRA, 2024)

LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. - Shizhe Diao; Rui Pan; Hanze Dong; Kashun Shum; Jipeng Zhang; Wei Xiong 015; Tong Zhang 001 (NAACL (Demonstrations), 2024)

R-Tuning: Instructing Large Language Models to Say 'I Don't Know'. - Hanning Zhang; Shizhe Diao; Yong Lin; Yi R. Fung 001; Qing Lian; Xingyao Wang 002; Yangyi Chen; Heng Ji; Tong Zhang 001 (NAACL-HLT, 2024)

Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization. - Zixuan Zhang; Revanth Gangi Reddy; Kevin Small; Tong Zhang 001; Heng Ji (NAACL-HLT (Findings), 2024)

PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs. - Ying Su; Jipeng Zhang; Yangqiu Song; Tong Zhang 001 (*SEM@NAACL, 2024)

Short-Term Load Forecasting Using Regularized Greedy Forest-Based Ensemble Model. - Binnie Wai-Keung Yiu; Tong Zhang 001; Cheuk-Wing Lee (IEEE Access, 2024)

AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets. - Ernest Perkowski; Rui Pan; Tuan Dung Nguyen; Yuan-Sen Ting; Sandor Kruk; Tong Zhang 001; Charlie O'Neill; Maja Jablonska; Zechang Sun; Michael J. Smith 013; Huiling Liu; Kevin Schawinski; et al. (CoRR, 2024)

MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance. - Renjie Pi; Tianyang Han; Yueqi Xie; Rui Pan; Qing Lian; Hanze Dong; Jipeng Zhang; Tong Zhang 001 (CoRR, 2024)

The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness. - Yifan Hao; Tong Zhang 001 (CoRR, 2024)

The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs. - Tianyang Han; Qing Lian; Rui Pan; Renjie Pi; Jipeng Zhang; Shizhe Diao; Yong Lin; Tong Zhang 001 (CoRR, 2024)

A Theoretical Analysis of Nash Learning from Human Feedback under General KL-Regularized Preference. - Chenlu Ye; Wei Xiong 015; Yuheng Zhang; Nan Jiang 008; Tong Zhang 001 (CoRR, 2024)

EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion. - Ying Su; Tianqing Fang; Huiru Xiao; Weiqi Wang 001; Yangqiu Song; Tong Zhang 001; Lei Chen 002 (CoRR, 2024)

An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling. - Xunpeng Huang; Hanze Dong; Difan Zou; Tong Zhang 001 (CoRR, 2024)

Do CLIPs Always Generalize Better than ImageNet Models? - Qizhou Wang; Yong Lin; Yongqiang Chen 002; Ludwig Schmidt; Bo Han 003; Tong Zhang 001 (CoRR, 2024)

On the Benefits of Over-parameterization for Out-of-Distribution Generalization. - Yifan Hao; Yong Lin; Difan Zou; Tong Zhang 001 (CoRR, 2024)

LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning. - Rui Pan; Xiang Liu; Shizhe Diao; Renjie Pi; Jipeng Zhang; Chi Han; Tong Zhang 001 (CoRR, 2024)

Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm. - Miao Lu; Han Zhong 001; Tong Zhang 001; Jose H. Blanchet (CoRR, 2024)

RLHF Workflow: From Reward Modeling to Online RLHF. - Hanze Dong; Wei Xiong 015; Bo Pang 004; Haoxiang Wang 003; Han Zhao 002; Yingbo Zhou; Nan Jiang 008; Doyen Sahoo; Caiming Xiong; Tong Zhang 001 (CoRR, 2024)

Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. - Xunpeng Huang; Difan Zou; Hanze Dong; Yi Zhang; Yi-An Ma; Tong Zhang 001 (CoRR, 2024)

Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs. - Rui Yang 010; Ruomeng Ding; Yong Lin; Huan Zhang; Tong Zhang 001 (CoRR, 2024)

VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning. - Cheng Niu; Yang Guan; Yuanhao Wu; Juno Zhu; Juntong Song; Randy Zhong; Kaihua Zhu; Siliang Xu; Shizhe Diao; Tong Zhang 001 (CoRR, 2024)

Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts. - Haoxiang Wang 003; Wei Xiong 015; Tengyang Xie; Han Zhao 002; Tong Zhang 001 (CoRR, 2024)

Large Batch Analysis for Adagrad Under Anisotropic Smoothness. - Yuxing Liu; Rui Pan; Tong Zhang 001 (CoRR, 2024)

ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting. - Rui Pan; Jipeng Zhang; Xingyuan Pan; Renjie Pi; Xiaoyu Wang 008; Tong Zhang 001 (CoRR, 2024)

TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts. - Ruida Wang; Jipeng Zhang; Yizhen Jia; Rui Pan; Shizhe Diao; Renjie Pi; Tong Zhang 001 (CoRR, 2024)

SmartQuant: CXL-based AI Model Store in Support of Runtime Configurable Weight Quantization. - Rui Xie 003; Asad Ul Haq; Linsen Ma; Krystal Sun; Sanchari Sen; Swagath Venkataramani; Liu Liu; Tong Zhang 001 (CoRR, 2024)

Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning. - Shuang Qiu; Dake Zhang; Rui Yang 010; Boxiang Lyu; Tong Zhang 001 (CoRR, 2024)

Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic. - Yifei He; Yuzheng Hu; Yong Lin; Tong Zhang 001; Han Zhao 002 (CoRR, 2024)

Nonsmooth Optimization over the Stiefel Manifold and Beyond: Proximal Gradient Method and Recent Variants. - Shixiang Chen; Shiqian Ma; Anthony Man-Cho So; Tong Zhang 001 (SIAM Rev., 2024)

On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. - Jianyu Wang; Rudrajit Das; Gauri Joshi; Satyen Kale; Zheng Xu 002; Tong Zhang 001 (Trans. Mach. Learn. Res., 2024)