Zhang, Zhihua

91 publications

NeurIPS 2025 A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation Yang Peng, Kaicheng Jin, Liangyu Zhang, Zhihua Zhang

NeurIPS 2025 Follow-the-Perturbed-Leader Nearly Achieves Best-of-Both-Worlds for the M-Set Semi-Bandit Problems Jingxin Zhan, Yuchen Xin, Chenjie Sun, Zhihua Zhang

JMLR 2025 On the Convergence of Projected Policy Gradient for Any Constant Step Sizes Jiacai Liu, Wenye Li, Dachao Lin, Ke Wei, Zhihua Zhang

JMLR 2024 A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness Yuze Han, Xiang Li, Shiyun Lin, Zhihua Zhang

MLJ 2024 Fedpower: Privacy-Preserving Distributed Eigenspace Estimation Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang, Zhihua Zhang

JMLR 2024 Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction Yuze Han, Guangzeng Xie, Zhihua Zhang

NeurIPS 2024 Statistical Efficiency of Distributional Temporal Difference Learning Yang Peng, Liangyu Zhang, Zhihua Zhang

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

NeurIPS 2023 Diff-Instruct: A Universal Approach for Transferring Knowledge from Pre-Trained Diffusion Models Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, Zhihua Zhang

NeurIPS 2023 Enhancing Adversarial Robustness via Score-Based Optimization Boya Zhang, Weijian Luo, Zhihua Zhang

NeurIPS 2023 Entropy-Based Training Methods for Scalable Neural Implicit Samplers Weijian Luo, Boya Zhang, Zhihua Zhang

ICML 2023 Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes Chuhan Xie, Wenhao Yang, Zhihua Zhang

NeurIPS 2023 Stochastic Distributed Optimization Under Average Second-Order Similarity: Algorithms and Analysis Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang

AISTATS 2022 Federated Reinforcement Learning with Environment Heterogeneity Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang

NeurIPS 2022 A Statistical Online Inference Approach in Averaged Stochastic Approximation Chuhan Xie, Zhihua Zhang

NeurIPS 2022 Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang

JMLR 2022 Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods Dachao Lin, Haishan Ye, Zhihua Zhang

ICML 2022 On Non-Local Convergence Analysis of Deep Linear Networks Kun Chen, Dachao Lin, Zhihua Zhang

NeurIPS 2022 Personalized Federated Learning Towards Communication Efficiency, Robustness and Fairness Shiyun Lin, Yuze Han, Xiang Li, Zhihua Zhang

NeurIPS 2022 Semi-Infinitely Constrained Markov Decision Processes Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang

COLT 2022 Statistical Estimation and Online Inference via Local SGD Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang

JMLR 2021 Approximate Newton Methods Haishan Ye, Luo Luo, Zhihua Zhang

ICML 2021 Communication-Efficient Distributed SVD via Local Power Iterations Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang

NeurIPS 2021 Faster Directional Convergence of Linear Neural Networks Under Spherically Symmetric Data Dachao Lin, Ruoyu Sun, Zhihua Zhang

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

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

AISTATS 2020 Efficient Spectrum-Revealing CUR Matrix Decomposition Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu

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

JMLR 2020 Nesterov's Acceleration for Approximate Newton Haishan Ye, Luo Luo, Zhihua Zhang

ICLR 2020 On the Convergence of FedAvg on Non-IID Data Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang

NeurIPS 2019 A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning Wenhao Yang, Xiang Li, Zhihua Zhang

ICML 2019 Lipschitz Generative Adversarial Nets Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang

JMLR 2019 Robust Frequent Directions with Application in Online Learning Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang

AAAI 2018 Attention-via-Attention Neural Machine Translation Shenjian Zhao, Zhihua Zhang

ICML 2017 Approximate Newton Methods and Their Local Convergence Haishan Ye, Luo Luo, Zhihua Zhang

AISTATS 2017 CPSG-MCMC: Clustering-Based Preprocessing Method for Stochastic Gradient MCMC Tianfan Fu, Zhihua Zhang

AAAI 2017 Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features Zihao Chen, Luo Luo, Zhihua Zhang

AAAI 2016 A Scalable and Extensible Framework for Superposition-Structured Models Shenjian Zhao, Cong Xie, Zhihua Zhang

IJCAI 2016 Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA Qiaomin Ye, Luo Luo, Zhihua Zhang

UAI 2016 Quasi-Newton Hamiltonian Monte Carlo Tianfan Fu, Luo Luo, Zhihua Zhang

JMLR 2016 SPSD Matrix Approximation Vis Column Selection: Theories, Algorithms, and Extensions Shusen Wang, Luo Luo, Zhihua Zhang

JMLR 2016 Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition Shusen Wang, Zhihua Zhang, Tong Zhang

AAAI 2016 Wishart Mechanism for Differentially Private Principal Components Analysis Wuxuan Jiang, Cong Xie, Zhihua Zhang

IJCAI 2015 A Scalable Community Detection Algorithm for Large Graphs Using Stochastic Block Models Chengbin Peng, Zhihua Zhang, Ka-Chun Wong, Xiangliang Zhang, David E. Keyes

ICML 2015 Support Matrix Machines Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li

NeurIPS 2014 Distributed Power-Law Graph Computing: Theoretical and Empirical Analysis Cong Xie, Ling Yan, Wu-Jun Li, Zhihua Zhang

AISTATS 2014 Efficient Algorithms and Error Analysis for the Modified Nystrom Method Shusen Wang, Zhihua Zhang

AAAI 2014 Exact Subspace Clustering in Linear Time Shusen Wang, Bojun Tu, Congfu Xu, Zhihua Zhang

ICML 2014 Making Fisher Discriminant Analysis Scalable Bojun Tu, Zhihua Zhang, Shusen Wang, Hui Qian

MLJ 2014 The Matrix Ridge Approximation: Algorithms and Applications Zhihua Zhang

AAAI 2014 Using the Matrix Ridge Approximation to Speedup Determinantal Point Processes Sampling Algorithms Shusen Wang, Chao Zhang, Hui Qian, Zhihua Zhang

AAAI 2013 A Concave Conjugate Approach for Nonconvex Penalized Regression with the MCP Penalty Shubao Zhang, Hui Qian, Wei Chen, Zhihua Zhang

ECML-PKDD 2013 A Nearly Unbiased Matrix Completion Approach Dehua Liu, Tengfei Zhou, Hui Qian, Congfu Xu, Zhihua Zhang

IJCAI 2013 A Scalable Approach to Column-Based Low-Rank Matrix Approximation Yifan Pi, Haoruo Peng, Shuchang Zhou, Zhihua Zhang

JMLR 2013 Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling Shusen Wang, Zhihua Zhang

AAAI 2013 Large-Scale Hierarchical Classification via Stochastic Perceptron Dehua Liu, Bojun Tu, Hui Qian, Zhihua Zhang

IJCAI 2013 Nonconvex Relaxation Approaches to Robust Matrix Recovery Shusen Wang, Dehua Liu, Zhihua Zhang

NeurIPS 2012 A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound Shusen Wang, Zhihua Zhang

AISTATS 2012 An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling Zhihua Zhang, Dakan Wang, Edward Chang

JMLR 2012 Coherence Functions with Applications in Large-Margin Classification Methods Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan

AAAI 2012 Colorization by Matrix Completion Shusen Wang, Zhihua Zhang

JMLR 2012 EP-GIG Priors and Applications in Bayesian Sparse Learning Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan

NeurIPS 2012 Nonconvex Penalization Using Laplace Exponents and Concave Conjugates Zhihua Zhang, Bojun Tu

ECML-PKDD 2012 Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets Haoruo Peng, Zhengyu Wang, Edward Y. Chang, Shuchang Zhou, Zhihua Zhang

AAAI 2011 A Fast Spectral Relaxation Approach to Matrix Completion via Kronecker Products Hui Zhao, Jiuqiang Han, Naiyan Wang, Congfu Xu, Zhihua Zhang

AAAI 2011 A Feasible Nonconvex Relaxation Approach to Feature Selection Cuixia Gao, Naiyan Wang, Qi Rose Yu, Zhihua Zhang

CVPR 2011 A Non-Convex Relaxation Approach to Sparse Dictionary Learning Jianping Shi, Xiang Ren, Guang Dai, Jingdong Wang, Zhihua Zhang

JMLR 2011 Bayesian Generalized Kernel Mixed Models Zhihua Zhang, Guang Dai, Michael I. Jordan

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

AISTATS 2010 Bayesian Generalized Kernel Models Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan

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

JMLR 2010 Regularized Discriminant Analysis, Ridge Regression and Beyond Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan

CVPR 2010 Sparse Representation Using Nonnegative Curds and Whey Yanan Liu, Fei Wu, Zhihua Zhang, Yueting Zhuang, Shuicheng Yan

ECML-PKDD 2010 Sparse Unsupervised Dimensionality Reduction Algorithms Wenjun Dou, Guang Dai, Congfu Xu, Zhihua Zhang

ECML-PKDD 2009 A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis Zhihua Zhang, Guang Dai, Michael I. Jordan

AISTATS 2009 Coherence Functions for Multicategory Margin-Based Classification Methods Zhihua Zhang, Michael Jordan, Wu-Jun Li, Dit-Yan Yeung

AISTATS 2009 Latent Variable Models for Dimensionality Reduction Zhihua Zhang, Michael I. Jordan

AISTATS 2009 Latent Wishart Processes for Relational Kernel Learning Wu-Jun Li, Zhihua Zhang, Dit-Yan Yeung

NeurIPS 2009 Optimal Scoring for Unsupervised Learning Zhihua Zhang, Guang Dai

NeurIPS 2009 Probabilistic Relational PCA Wu-jun Li, Dit-Yan Yeung, Zhihua Zhang

NeurIPS 2008 Posterior Consistency of the Silverman G-Prior in Bayesian Model Choice Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung

MLJ 2007 Surrogate Maximization/minimization Algorithms and Extensions Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

UAI 2006 Bayesian Multicategory Support Vector Machines Zhihua Zhang, Michael I. Jordan

MLJ 2006 Model-Based Transductive Learning of the Kernel Matrix Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

ECML-PKDD 2005 Annealed Discriminant Analysis Gang Wang, Zhihua Zhang, Frederick H. Lochovsky

ICML 2004 Bayesian Inference for Transductive Learning of Kernel Matrix Using the Tanner-Wong Data Augmentation Algorithm Zhihua Zhang, Dit-Yan Yeung, James T. Kwok

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

ICML 2004 Surrogate Maximization/minimization Algorithms for AdaBoost and the Logistic Regression Model Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

CVPR 2003 Bootstrapping SVM Active Learning by Incorporating Unlabelled Images for Image Retrieval Lei Wang, Kap Luk Chan, Zhihua Zhang

ICML 2003 Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation Zhihua Zhang

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