Parametric Distance Metric Learning with Label Information. - Zhihua Zhang 004; James T. Kwok; Dit-Yan Yeung (IJCAI, 2003)

Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo. - Zhihua Zhang 004; Kap Luk Chan; James T. Kwok; Dit-Yan Yeung (AAAI, 2004)

Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model. - Zhihua Zhang 004; James T. Kwok; Dit-Yan Yeung (ICML, 2004)

Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm. - Zhihua Zhang 004; Dit-Yan Yeung; James T. Kwok (ICML, 2004)

Annealed Discriminant Analysis. - Gang Wang 004; Zhihua Zhang 004; Frederick H. Lochovsky (ECML, 2005)

A Bernoulli Relational Model for Nonlinear Embedding. - Gang Wang 004; Hui Zhang; Zhihua Zhang 004; Frederick H. Lochovsky (ICDM, 2005)

Learning with non-metric proximity matrices. - Gang Wu 005; Edward Y. Chang; Zhihua Zhang 004 (ACM Multimedia, 2005)

Kronecker Factorization for Speeding up Kernel Machines. - Gang Wu 005; Zhihua Zhang 004; Edward Y. Chang (SDM, 2005)

Adaptive non-linear clustering in data streams. - Ankur Jain; Zhihua Zhang 004; Edward Y. Chang (CIKM, 2006)

Bayesian Multicategory Support Vector Machines. - Zhihua Zhang 004; Michael I. Jordan (UAI, 2006)

Model-based transductive learning of the kernel matrix. - Zhihua Zhang 004; James T. Kwok; Dit-Yan Yeung (Mach. Learn., 2006)

Surrogate maximization/minimization algorithms and extensions. - Zhihua Zhang 004; James T. Kwok; Dit-Yan Yeung (Mach. Learn., 2007)

Semiparametric Regression Using Student t Processes. - Zhihua Zhang 004; Gang Wu 005; Edward Y. Chang (IEEE Trans. Neural Networks, 2007)

Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. - Zhihua Zhang 004; Michael I. Jordan; Dit-Yan Yeung (NIPS, 2008)

Probabilistic Relational PCA. - Wu-Jun Li; Dit-Yan Yeung; Zhihua Zhang 004 (NIPS, 2009)

A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. - Zhihua Zhang 004; Guang Dai; Michael I. Jordan (ECML/PKDD (2), 2009)

Latent Wishart Processes for Relational Kernel Learning. - Wu-Jun Li; Zhihua Zhang 004; Dit-Yan Yeung (AISTATS, 2009)

Latent Variable Models for Dimensionality Reduction. - Zhihua Zhang 004; Michael I. Jordan (AISTATS, 2009)

Coherence Functions for Multicategory Margin-based Classification Methods. - Zhihua Zhang 004; Michael I. Jordan; Wu-Jun Li; Dit-Yan Yeung (AISTATS, 2009)

Matrix-Variate Dirichlet Process Mixture Models. - Zhihua Zhang 004; Guang Dai; Michael I. Jordan (AISTATS, 2010)

Bayesian Generalized Kernel Models. - Zhihua Zhang 004; Guang Dai; Donghui Wang; Michael I. Jordan (AISTATS, 2010)

Regularized Discriminant Analysis, Ridge Regression and Beyond. - Zhihua Zhang 004; Guang Dai; Congfu Xu; Michael I. Jordan (J. Mach. Learn. Res., 2010)

A regularization framework for multiclass classification: A deterministic annealing approach. - Zhihua Zhang 004; Gang Wang 004; Dit-Yan Yeung; Guang Dai; Frederick H. Lochovsky (Pattern Recognit., 2010)

Generalized Latent Factor Models for Social Network Analysis. - Wu-Jun Li; Dit-Yan Yeung; Zhihua Zhang 004 (IJCAI, 2011)

Bayesian Generalized Kernel Mixed Models. - Zhihua Zhang 004; Guang Dai; Michael I. Jordan (J. Mach. Learn. Res., 2011)

Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets. - Haoruo Peng; Zhengyu Wang; Edward Y. Chang; Shuchang Zhou 001; Zhihua Zhang 004 (ECML/PKDD (1), 2012)

Coherence functions with applications in large-margin classification methods. - Zhihua Zhang 004; Dehua Liu; Guang Dai; Michael I. Jordan (J. Mach. Learn. Res., 2012)

An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling. - Zhihua Zhang 004; Dakan Wang; Edward Y. Chang (AISTATS, 2012)

EP-GIG Priors and Applications in Bayesian Sparse Learning. - Zhihua Zhang 004; Shusen Wang; Dehua Liu; Michael I. Jordan (J. Mach. Learn. Res., 2012)

A Scalable Approach to Column-Based Low-Rank Matrix Approximation. - Yifan Pi; Haoruo Peng; Shuchang Zhou 001; Zhihua Zhang 004 (IJCAI, 2013)

Multicategory large margin classification methods: Hinge losses vs. coherence functions. - Zhihua Zhang 004; Cheng Chen 015; Guang Dai; Wu-Jun Li; Dit-Yan Yeung (Artif. Intell., 2014)

The Modified Nystrom Method: Theories, Algorithms, and Extension. - Shusen Wang; Luo Luo; Zhihua Zhang 004 (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)

Group Orbit Optimization: A Unified Approach to Data Normalization. - Shuchang Zhou 001; Zhihua Zhang 004; Xiaobing Feng 002 (CoRR, 2014)

Support Matrix Machines. - Luo Luo; Yubo Xie; Zhihua Zhang 004; Wu-Jun Li (ICML, 2015)

A Parallel algorithm for $ mathcal{X}$-Armed bandits. - Cheng Chen 015; Shuang Liu; Zhihua Zhang 004; Wu-Jun Li (CoRR, 2015)

Distributed Multi-Armed Bandits: Regret vs. Communication. - Shuang Liu; Cheng Chen 015; Zhihua Zhang 004 (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)

Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA. - Qiaomin Ye; Luo Luo; Zhihua Zhang 004 (IJCAI, 2016)

Quasi-Newton Hamiltonian Monte Carlo. - Tianfan Fu; Luo Luo; Zhihua Zhang 004 (UAI, 2016)

Variance-Reduced Second-Order Methods. - Luo Luo; Zihao Chen; Zhihua Zhang 004; Wu-Jun Li (CoRR, 2016)

Revisiting Sub-sampled Newton Methods. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (CoRR, 2016)

Tighter bound of Sketched Generalized Matrix Approximation. - Haishan Ye; Qiaoming Ye; Zhihua Zhang 004 (CoRR, 2016)

SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions. - Shusen Wang; Luo Luo; Zhihua Zhang 004 (J. Mach. Learn. Res., 2016)

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

Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. - Zihao Chen; Luo Luo; Zhihua Zhang 004 (AAAI, 2017)

Approximate Newton Methods and Their Local Convergence. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (ICML, 2017)

Online Learning Via Regularized Frequent Directions. - Luo Luo; Cheng Chen 015; Zhihua Zhang 004; Wu-Jun Li (CoRR, 2017)

A Unifying Framework for Convergence Analysis of Approximate Newton Methods. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (CoRR, 2017)

Nesterov's Acceleration For Approximate Newton. - Haishan Ye; Zhihua Zhang 004 (CoRR, 2017)

Fast Fisher discriminant analysis with randomized algorithms. - Haishan Ye; Yujun Li; Cheng Chen 015; Zhihua Zhang 004 (Pattern Recognit., 2017)

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 001 (KDD, 2018)

Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks. - Guangzeng Xie; Yitan Wang; Shuchang Zhou 001; Zhihua Zhang 004 (CoRR, 2018)

Lipschitz Generative Adversarial Nets. - Zhiming Zhou 001; Jiadong Liang; Yuxuan Song; Lantao Yu; Hongwei Wang 004; Weinan Zhang 001; Yong Yu 001; Zhihua Zhang 004 (ICML, 2019)

A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning. - Wenhao Yang; Xiang Li 050; Zhihua Zhang 004 (NeurIPS, 2019)

A Unified Framework for Regularized Reinforcement Learning. - Xiang Li 050; Wenhao Yang; Zhihua Zhang 004 (CoRR, 2019)

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks. - Hao Jin; Dachao Lin; Zhihua Zhang 004 (CoRR, 2019)

A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization. - Guangzeng Xie; Luo Luo; Zhihua Zhang 004 (CoRR, 2019)

A Stochastic Proximal Point Algorithm for Saddle-Point Problems. - Luo Luo; Cheng Chen 015; Yujun Li; Guangzeng Xie; Zhihua Zhang 004 (CoRR, 2019)

Communication Efficient Decentralized Training with Multiple Local Updates. - Xiang Li 050; Wenhao Yang; Shusen Wang; Zhihua Zhang 004 (CoRR, 2019)

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

Fast stochastic second-order method logarithmic in condition number. - Haishan Ye; Guangzeng Xie; Luo Luo; Zhihua Zhang 004 (Pattern Recognit., 2019)

Do Subsampled Newton Methods Work for High-Dimensional Data? - Xiang Li 050; Shusen Wang; Zhihua Zhang 004 (AAAI, 2020)

Efficient Spectrum-Revealing CUR Matrix Decomposition. - Cheng Chen 015; Ming Gu; Zhihua Zhang 004; Weinan Zhang 001; Yong Yu 001 (AISTATS, 2020)

Active Learning Approaches to Enhancing Neural Machine Translation: An Empirical Study. - Yuekai Zhao; Haoran Zhang; Shuchang Zhou 001; Zhihua Zhang 004 (EMNLP (Findings), 2020)

On the Convergence of FedAvg on Non-IID Data. - Xiang Li 050; Kaixuan Huang; Wenhao Yang; Shusen Wang; Zhihua Zhang 004 (ICLR, 2020)

Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. - Guangzeng Xie; Luo Luo; Yijiang Lian; Zhihua Zhang 004 (ICML, 2020)

Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond. - Dachao Lin; Peiqin Sun; Guangzeng Xie; Shuchang Zhou 001; Zhihua Zhang 004 (CoRR, 2020)

Landscape of Sparse Linear Network: A Brief Investigation. - Dachao Lin; Ruoyu Sun 001; Zhihua Zhang 004 (CoRR, 2020)

Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs. - Wenhao Yang; Xiang Li 050; Guangzeng Xie; Zhihua Zhang 004 (CoRR, 2020)

Nesterov's Acceleration for Approximate Newton. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (J. Mach. Learn. Res., 2020)

Multi-split Reversible Transformers Can Enhance Neural Machine Translation. - Yuekai Zhao; Shuchang Zhou 001; Zhihua Zhang 004 (EACL, 2021)

Communication-Efficient Distributed SVD via Local Power Iterations. - Xiang Li 050; Shusen Wang; Kun Chen; Zhihua Zhang 004 (ICML, 2021)

Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data. - Dachao Lin; Ruoyu Sun 001; Zhihua Zhang 004 (NeurIPS, 2021)

Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. - Dachao Lin; Haishan Ye; Zhihua Zhang 004 (NeurIPS, 2021)

Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications. - Xiang Li 050; Zhihua Zhang 004 (CoRR, 2021)

Privacy-Preserving Distributed SVD via Federated Power. - Xiao Guo; Xiang Li 050; Xiangyu Chang; Shusen Wang; Zhihua Zhang 004 (CoRR, 2021)

Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent. - Guangzeng Xie; Hao Jin; Dachao Lin; Zhihua Zhang 004 (CoRR, 2021)

Directional Convergence Analysis under Spherically Symmetric Distribution. - Dachao Lin; Zhihua Zhang 004 (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)

Statistical Estimation and Inference via Local SGD in Federated Learning. - Xiang Li 050; Jiadong Liang; Xiangyu Chang; Zhihua Zhang 004 (CoRR, 2021)

Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations. - Haishan Ye; Dachao Lin; Zhihua Zhang 004 (CoRR, 2021)

Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient. - Xiang Li 050; Wenhao Yang; Zhihua Zhang 004; Michael I. Jordan (CoRR, 2021)

Approximate Newton Methods. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (J. Mach. Learn. Res., 2021)

Accelerated Proximal Subsampled Newton Method. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (IEEE Trans. Neural Networks Learn. Syst., 2021)

Federated Reinforcement Learning with Environment Heterogeneity. - Hao Jin; Yang Peng; Wenhao Yang; Shusen Wang; Zhihua Zhang 004 (AISTATS, 2022)

Statistical Estimation and Online Inference via Local SGD. - Xiang Li 050; Jiadong Liang; Xiangyu Chang; Zhihua Zhang 004 (COLT, 2022)

On Non-local Convergence Analysis of Deep Linear Networks. - Kun Chen; Dachao Lin; Zhihua Zhang 004 (ICML, 2022)

Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. - Jiadong Liang; Yuze Han; Xiang Li 050; Zhihua Zhang 004 (NeurIPS, 2022)

Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. - Shiyun Lin; Yuze Han; Xiang Li 050; Zhihua Zhang 004 (NeurIPS, 2022)

Semi-infinitely Constrained Markov Decision Processes. - Liangyu Zhang; Yang Peng; Wenhao Yang; Zhihua Zhang 004 (NeurIPS, 2022)

On the landscape of one-hidden-layer sparse networks and beyond. - Dachao Lin; Ruoyu Sun 001; Zhihua Zhang 004 (Artif. Intell., 2022)

Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer. - Kun Chen; Dachao Lin; Zhihua Zhang 004 (CoRR, 2022)

On the Convergence of Policy in Unregularized Policy Mirror Descent. - Dachao Lin; Zhihua Zhang 004 (CoRR, 2022)

Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods. - Dachao Lin; Haishan Ye; Zhihua Zhang 004 (J. Mach. Learn. Res., 2022)

A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. - Xiang Li 050; Wenhao Yang; Jiadong Liang; Zhihua Zhang 004; Michael I. Jordan (AISTATS, 2023)

Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants. - Jiadong Liang; Yuze Han; Xiang Li 050; Zhihua Zhang 004 (Allerton, 2023)

Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. - Dachao Lin; Yuze Han; Haishan Ye; Zhihua Zhang 004 (NeurIPS, 2023)

Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning. - Liangyu Zhang; Yang Peng; Wenhao Yang; Zhihua Zhang 004 (CoRR, 2023)

Estimation and Inference in Distributional Reinforcement Learning. - Liangyu Zhang; Yang Peng; Jiadong Liang; Wenhao Yang; Zhihua Zhang 004 (CoRR, 2023)

Towards explicit superlinear convergence rate for SR1. - Haishan Ye; Dachao Lin; Xiangyu Chang; Zhihua Zhang 004 (Math. Program., 2023)

Statistical Efficiency of Distributional Temporal Difference Learning. - Yang Peng; Liangyu Zhang; Zhihua Zhang 004 (NeurIPS, 2024)

Near Minimax-Optimal Distributional Temporal Difference Algorithms and The Freedman Inequality in Hilbert Spaces. - Yang Peng; Liangyu Zhang; Zhihua Zhang 004 (CoRR, 2024)

Federated Control in Markov Decision Processes. - Hao Jin; Yang Peng; Liangyu Zhang; Zhihua Zhang 004 (CoRR, 2024)

Semi-Infinitely Constrained Markov Decision Processes and Provably Efficient Reinforcement Learning. - Liangyu Zhang; Yang Peng; Wenhao Yang; Zhihua Zhang 004 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Fedpower: privacy-preserving distributed eigenspace estimation. - Xiao Guo; Xiang Li 050; Xiangyu Chang; Shusen Wang; Zhihua Zhang 004 (Mach. Learn., 2024)