Zhihua Zhang - RankMe

118 papers A* 28A 14Journal 69Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	On the Convergence of Projected Policy Gradient for Any Constant Step Sizes. J. Mach. Learn. Res. Jiacai Liu, Wenye Li, Dachao Lin, Ke Wei, Zhihua Zhang
2024	J	`jnl`	A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness. J. Mach. Learn. Res. Yuze Han, Xiang Li, Shiyun Lin, Zhihua Zhang
2024	J	`jnl`	Federated Control in Markov Decision Processes. CoRR Hao Jin, Yang Peng, Liangyu Zhang, Zhihua Zhang
2024	J	`jnl`	Fedpower: privacy-preserving distributed eigenspace estimation. Mach. Learn. Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang, Zhihua Zhang
2024	J	`jnl`	Near Minimax-Optimal Distributional Temporal Difference Algorithms and The Freedman Inequality in Hilbert Spaces. CoRR Yang Peng, Liangyu Zhang, Zhihua Zhang
2024	J	`jnl`	Semi-Infinitely Constrained Markov Decision Processes and Provably Efficient Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang
2024	A*	`conf`	Statistical Efficiency of Distributional Temporal Difference Learning. NeurIPS Yang Peng, Liangyu Zhang, Zhihua Zhang
2023	A	`conf`	A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. AISTATS Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan
2023	—	`conf`	Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants. Allerton Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang
2023	J	`jnl`	Estimation and Inference in Distributional Reinforcement Learning. CoRR Liangyu Zhang, Yang Peng, Jiadong Liang, Wenhao Yang, Zhihua Zhang
2023	J	`jnl`	Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning. CoRR Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang
2023	A*	`conf`	Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. NeurIPS Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang
2023	J	`jnl`	Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. CoRR Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang
2023	J	`jnl`	Towards explicit superlinear convergence rate for SR1. Math. Program. Haishan Ye, Dachao Lin, Xiangyu Chang, Zhihua Zhang
2022	A*	`conf`	Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. NeurIPS Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang
2022	J	`jnl`	Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods. J. Mach. Learn. Res. Dachao Lin, Haishan Ye, Zhihua Zhang
2022	A	`conf`	Federated Reinforcement Learning with Environment Heterogeneity. AISTATS Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang
2022	J	`jnl`	Federated Reinforcement Learning with Environment Heterogeneity. CoRR Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang
2022	J	`jnl`	Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer. CoRR Kun Chen, Dachao Lin, Zhihua Zhang
2022	A*	`conf`	On Non-local Convergence Analysis of Deep Linear Networks. ICML Kun Chen, Dachao Lin, Zhihua Zhang
2022	J	`jnl`	On the Convergence of Policy in Unregularized Policy Mirror Descent. CoRR Dachao Lin, Zhihua Zhang
2022	J	`jnl`	On the landscape of one-hidden-layer sparse networks and beyond. Artif. Intell. Dachao Lin, Ruoyu Sun, Zhihua Zhang
2022	A*	`conf`	Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. NeurIPS Shiyun Lin, Yuze Han, Xiang Li, Zhihua Zhang
2022	A*	`conf`	Semi-infinitely Constrained Markov Decision Processes. NeurIPS Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang
2022	A*	`conf`	Statistical Estimation and Online Inference via Local SGD. COLT Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang
2021	J	`jnl`	Accelerated Proximal Subsampled Newton Method. IEEE Trans. Neural Networks Learn. Syst. Haishan Ye, Luo Luo, Zhihua Zhang
2021	J	`jnl`	Approximate Newton Methods. J. Mach. Learn. Res. Haishan Ye, Luo Luo, Zhihua Zhang
2021	A*	`conf`	Communication-Efficient Distributed SVD via Local Power Iterations. ICML Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang
2021	J	`jnl`	Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications. CoRR Xiang Li, Zhihua Zhang
2021	J	`jnl`	Directional Convergence Analysis under Spherically Symmetric Distribution. CoRR Dachao Lin, Zhihua Zhang
2021	A*	`conf`	Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data. NeurIPS Dachao Lin, Ruoyu Sun, Zhihua Zhang
2021	J	`jnl`	Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations. CoRR Haishan Ye, Dachao Lin, Zhihua Zhang
2021	A*	`conf`	Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. NeurIPS Dachao Lin, Haishan Ye, Zhihua Zhang
2021	J	`jnl`	Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent. CoRR Guangzeng Xie, Hao Jin, Dachao Lin, Zhihua Zhang
2021	A	`conf`	Multi-split Reversible Transformers Can Enhance Neural Machine Translation. EACL Yuekai Zhao, Shuchang Zhou, Zhihua Zhang
2021	J	`jnl`	Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization. CoRR Luo Luo, Guangzeng Xie, Tong Zhang, Zhihua Zhang
2021	J	`jnl`	Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient. CoRR Xiang Li, Wenhao Yang, Zhihua Zhang, Michael I. Jordan
2021	J	`jnl`	Privacy-Preserving Distributed SVD via Federated Power. CoRR Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang, Zhihua Zhang
2021	J	`jnl`	Statistical Estimation and Inference via Local SGD in Federated Learning. CoRR Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang
2020	—	`conf`	Active Learning Approaches to Enhancing Neural Machine Translation: An Empirical Study. EMNLP (Findings) Yuekai Zhao, Haoran Zhang, Shuchang Zhou, Zhihua Zhang
2020	J	`jnl`	Communication-Efficient Distributed SVD via Local Power Iterations. CoRR Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang
2020	A*	`conf`	Do Subsampled Newton Methods Work for High-Dimensional Data? AAAI Xiang Li, Shusen Wang, Zhihua Zhang
2020	A	`conf`	Efficient Spectrum-Revealing CUR Matrix Decomposition. AISTATS Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu
2020	J	`jnl`	Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs. CoRR Wenhao Yang, Xiang Li, Guangzeng Xie, Zhihua Zhang
2020	J	`jnl`	Landscape of Sparse Linear Network: A Brief Investigation. CoRR Dachao Lin, Ruoyu Sun, Zhihua Zhang
2020	A*	`conf`	Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. ICML Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang
2020	J	`jnl`	Nesterov's Acceleration for Approximate Newton. J. Mach. Learn. Res. Haishan Ye, Luo Luo, Zhihua Zhang
2020	A*	`conf`	On the Convergence of FedAvg on Non-IID Data. ICLR Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang
2020	J	`jnl`	Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond. CoRR Dachao Lin, Peiqin Sun, Guangzeng Xie, Shuchang Zhou, Zhihua Zhang
2019	J	`jnl`	A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization. CoRR Guangzeng Xie, Luo Luo, Zhihua Zhang
2019	A*	`conf`	A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning. NeurIPS Wenhao Yang, Xiang Li, Zhihua Zhang
2019	J	`jnl`	A Stochastic Proximal Point Algorithm for Saddle-Point Problems. CoRR Luo Luo, Cheng Chen, Yujun Li, Guangzeng Xie, Zhihua Zhang
2019	J	`jnl`	A Unified Framework for Regularized Reinforcement Learning. CoRR Xiang Li, Wenhao Yang, Zhihua Zhang
2019	J	`jnl`	Communication Efficient Decentralized Training with Multiple Local Updates. CoRR Xiang Li, Wenhao Yang, Shusen Wang, Zhihua Zhang
2019	J	`jnl`	Do Subsampled Newton Methods Work for High-Dimensional Data? CoRR Xiang Li, Shusen Wang, Zhihua Zhang
2019	J	`jnl`	Fast Generalized Matrix Regression with Applications in Machine Learning. CoRR Haishan Ye, Shusen Wang, Zhihua Zhang, Tong Zhang
2019	J	`jnl`	Fast stochastic second-order method logarithmic in condition number. Pattern Recognit. Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang
2019	A*	`conf`	Lipschitz Generative Adversarial Nets. ICML Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang
2019	J	`jnl`	Lipschitz Generative Adversarial Nets. CoRR Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang
2019	J	`jnl`	On the Convergence of FedAvg on Non-IID Data. CoRR Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang
2019	J	`jnl`	Robust Frequent Directions with Application in Online Learning. J. Mach. Learn. Res. Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang
2019	J	`jnl`	Towards Better Generalization: BP-SVRG in Training Deep Neural Networks. CoRR Hao Jin, Dachao Lin, Zhihua Zhang
2018	J	`jnl`	Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks. CoRR Guangzeng Xie, Yitan Wang, Shuchang Zhou, Zhihua Zhang
2018	A*	`conf`	Sketched Follow-The-Regularized-Leader for Online Factorization Machine. KDD Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, Jian Pei
2017	J	`jnl`	A Unifying Framework for Convergence Analysis of Approximate Newton Methods. CoRR Haishan Ye, Luo Luo, Zhihua Zhang
2017	A*	`conf`	Approximate Newton Methods and Their Local Convergence. ICML Haishan Ye, Luo Luo, Zhihua Zhang
2017	A*	`conf`	Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. AAAI Zihao Chen, Luo Luo, Zhihua Zhang
2017	J	`jnl`	Fast Fisher discriminant analysis with randomized algorithms. Pattern Recognit. Haishan Ye, Yujun Li, Cheng Chen, Zhihua Zhang
2017	J	`jnl`	Nesterov's Acceleration For Approximate Newton. CoRR Haishan Ye, Zhihua Zhang
2017	J	`jnl`	Online Learning Via Regularized Frequent Directions. CoRR Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li
2016	J	`jnl`	Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. CoRR Zihao Chen, Luo Luo, Zhihua Zhang
2016	A*	`conf`	Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA. IJCAI Qiaomin Ye, Luo Luo, Zhihua Zhang
2016	A	`conf`	Quasi-Newton Hamiltonian Monte Carlo. UAI Tianfan Fu, Luo Luo, Zhihua Zhang
2016	J	`jnl`	Revisiting Sub-sampled Newton Methods. CoRR Haishan Ye, Luo Luo, Zhihua Zhang
2016	J	`jnl`	SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions. J. Mach. Learn. Res. Shusen Wang, Luo Luo, Zhihua Zhang
2016	J	`jnl`	Tighter bound of Sketched Generalized Matrix Approximation. CoRR Haishan Ye, Qiaoming Ye, Zhihua Zhang
2016	J	`jnl`	Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition. J. Mach. Learn. Res. Shusen Wang, Zhihua Zhang, Tong Zhang
2016	J	`jnl`	Variance-Reduced Second-Order Methods. CoRR Luo Luo, Zihao Chen, Zhihua Zhang, Wu-Jun Li
2015	J	`jnl`	A Parallel algorithm for $\mathcal{X}$-Armed bandits. CoRR Cheng Chen, Shuang Liu, Zhihua Zhang, Wu-Jun Li
2015	J	`jnl`	Distributed Multi-Armed Bandits: Regret vs. Communication. CoRR Shuang Liu, Cheng Chen, Zhihua Zhang
2015	J	`jnl`	Improved Analyses of the Randomized Power Method and Block Lanczos Method. CoRR Shusen Wang, Zhihua Zhang, Tong Zhang
2015	A*	`conf`	Support Matrix Machines. ICML Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li
2015	J	`jnl`	Towards More Efficient Nystrom Approximation and CUR Matrix Decomposition. CoRR Shusen Wang, Zhihua Zhang, Tong Zhang
2014	J	`jnl`	Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion. CoRR Shusen Wang, Tong Zhang, Zhihua Zhang
2014	J	`jnl`	Group Orbit Optimization: A Unified Approach to Data Normalization. CoRR Shuchang Zhou, Zhihua Zhang, Xiaobing Feng
2014	J	`jnl`	Multicategory large margin classification methods: Hinge losses vs. coherence functions. Artif. Intell. Zhihua Zhang, Cheng Chen, Guang Dai, Wu-Jun Li, Dit-Yan Yeung
2014	J	`jnl`	The Modified Nystrom Method: Theories, Algorithms, and Extension. CoRR Shusen Wang, Luo Luo, Zhihua Zhang
2013	A*	`conf`	A Scalable Approach to Column-Based Low-Rank Matrix Approximation. IJCAI Yifan Pi, Haoruo Peng, Shuchang Zhou, Zhihua Zhang
2012	A	`conf`	An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling. AISTATS Zhihua Zhang, Dakan Wang, Edward Y. Chang
2012	J	`jnl`	Bayesian Multicategory Support Vector Machines. CoRR Zhihua Zhang, Michael I. Jordan
2012	J	`jnl`	Coherence functions with applications in large-margin classification methods. J. Mach. Learn. Res. Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan
2012	J	`jnl`	EP-GIG Priors and Applications in Bayesian Sparse Learning. J. Mach. Learn. Res. Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan
2012	—	`conf`	Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets. ECML/PKDD (1) Haoruo Peng, Zhengyu Wang, Edward Y. Chang, Shuchang Zhou, Zhihua Zhang
2011	J	`jnl`	Bayesian Generalized Kernel Mixed Models. J. Mach. Learn. Res. Zhihua Zhang, Guang Dai, Michael I. Jordan
2011	A*	`conf`	Generalized Latent Factor Models for Social Network Analysis. IJCAI Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang
2010	J	`jnl`	A regularization framework for multiclass classification: A deterministic annealing approach. Pattern Recognit. Zhihua Zhang, Gang Wang, Dit-Yan Yeung, Guang Dai, Frederick H. Lochovsky
2010	A	`conf`	Bayesian Generalized Kernel Models. AISTATS Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan
2010	A	`conf`	Matrix-Variate Dirichlet Process Mixture Models. AISTATS Zhihua Zhang, Guang Dai, Michael I. Jordan
2010	J	`jnl`	Regularized Discriminant Analysis, Ridge Regression and Beyond. J. Mach. Learn. Res. Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan
2009	—	`conf`	A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. ECML/PKDD (2) Zhihua Zhang, Guang Dai, Michael I. Jordan
2009	A	`conf`	Coherence Functions for Multicategory Margin-based Classification Methods. AISTATS Zhihua Zhang, Michael I. Jordan, Wu-Jun Li, Dit-Yan Yeung
2009	A	`conf`	Latent Variable Models for Dimensionality Reduction. AISTATS Zhihua Zhang, Michael I. Jordan
2009	A	`conf`	Latent Wishart Processes for Relational Kernel Learning. AISTATS Wu-Jun Li, Zhihua Zhang, Dit-Yan Yeung
2009	—	`conf`	Probabilistic Relational PCA. NIPS Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang
2008	—	`conf`	Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. NIPS Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung
2007	J	`jnl`	Semiparametric Regression Using Student t Processes. IEEE Trans. Neural Networks Zhihua Zhang, Gang Wu, Edward Y. Chang
2007	J	`jnl`	Surrogate maximization/minimization algorithms and extensions. Mach. Learn. Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
2006	A	`conf`	Adaptive non-linear clustering in data streams. CIKM Ankur Jain, Zhihua Zhang, Edward Y. Chang
2006	A	`conf`	Bayesian Multicategory Support Vector Machines. UAI Zhihua Zhang, Michael I. Jordan
2006	J	`jnl`	Model-based transductive learning of the kernel matrix. Mach. Learn. Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
2005	A*	`conf`	A Bernoulli Relational Model for Nonlinear Embedding. ICDM Gang Wang, Hui Zhang, Zhihua Zhang, Frederick H. Lochovsky
2005	—	`conf`	Annealed Discriminant Analysis. ECML Gang Wang, Zhihua Zhang, Frederick H. Lochovsky
2005	A	`conf`	Kronecker Factorization for Speeding up Kernel Machines. SDM Gang Wu, Zhihua Zhang, Edward Y. Chang
2005	A*	`conf`	Learning with non-metric proximity matrices. ACM Multimedia Gang Wu, Edward Y. Chang, Zhihua Zhang
2004	A*	`conf`	Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo. AAAI Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan Yeung
2004	A*	`conf`	Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm. ICML Zhihua Zhang, Dit-Yan Yeung, James T. Kwok
2004	A*	`conf`	Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model. ICML Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
2003	A*	`conf`	Parametric Distance Metric Learning with Label Information. IJCAI Zhihua Zhang, James T. Kwok, Dit-Yan Yeung