Zhihua Zhang

According to our database, Zhihua Zhang authored at least 108 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Fedpower: privacy-preserving distributed eigenspace estimation.

Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang, Zhihua Zhang

Mach. Learn., December, 2024

Semi-Infinitely Constrained Markov Decision Processes and Provably Efficient Reinforcement Learning.

Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang

IEEE Trans. Pattern Anal. Mach. Intell., May, 2024

Federated Control in Markov Decision Processes.

Hao Jin, Yang Peng, Liangyu Zhang, Zhihua Zhang

CoRR, 2024

Near Minimax-Optimal Distributional Temporal Difference Algorithms and The Freedman Inequality in Hilbert Spaces.

Yang Peng, Liangyu Zhang, Zhihua Zhang

CoRR, 2024

Statistical Efficiency of Distributional Temporal Difference Learning.

Yang Peng, Liangyu Zhang, Zhihua Zhang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Towards explicit superlinear convergence rate for SR1.

Haishan Ye, Dachao Lin, Xiangyu Chang, Zhihua Zhang

Math. Program., May, 2023

Estimation and Inference in Distributional Reinforcement Learning.

Liangyu Zhang, Yang Peng, Jiadong Liang, Wenhao Yang, Zhihua Zhang

CoRR, 2023

Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning.

Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang

CoRR, 2023

Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis.

Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants.

Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang

Proceedings of the 59th Annual Allerton Conference on Communication, 2023

A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning.

Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods.

Dachao Lin, Haishan Ye, Zhihua Zhang

J. Mach. Learn. Res., 2022

On the Convergence of Policy in Unregularized Policy Mirror Descent.

Dachao Lin, Zhihua Zhang

CoRR, 2022

Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer.

Kun Chen, Dachao Lin, Zhihua Zhang

CoRR, 2022

On the landscape of one-hidden-layer sparse networks and beyond.

Dachao Lin, Ruoyu Sun, Zhihua Zhang

Artif. Intell., 2022

Semi-infinitely Constrained Markov Decision Processes.

Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness.

Shiyun Lin, Yuze Han, Xiang Li, Zhihua Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective.

Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Non-local Convergence Analysis of Deep Linear Networks.

Kun Chen, Dachao Lin, Zhihua Zhang

Proceedings of the International Conference on Machine Learning, 2022

Statistical Estimation and Online Inference via Local SGD.

Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Federated Reinforcement Learning with Environment Heterogeneity.

Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Accelerated Proximal Subsampled Newton Method.

Haishan Ye, Luo Luo, Zhihua Zhang

IEEE Trans. Neural Networks Learn. Syst., 2021

Approximate Newton Methods.

Haishan Ye, Luo Luo, Zhihua Zhang

J. Mach. Learn. Res., 2021

Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient.

Xiang Li, Wenhao Yang, Zhihua Zhang, Michael I. Jordan

CoRR, 2021

Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations.

Haishan Ye, Dachao Lin, Zhihua Zhang

CoRR, 2021

Statistical Estimation and Inference via Local SGD in Federated Learning.

Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang

CoRR, 2021

Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization.

Luo Luo, Guangzeng Xie, Tong Zhang, Zhihua Zhang

CoRR, 2021

Directional Convergence Analysis under Spherically Symmetric Distribution.

Dachao Lin, Zhihua Zhang

CoRR, 2021

Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent.

Guangzeng Xie, Hao Jin, Dachao Lin, Zhihua Zhang

CoRR, 2021

Privacy-Preserving Distributed SVD via Federated Power.

Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang, Zhihua Zhang

CoRR, 2021

Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications.

Xiang Li, Zhihua Zhang

CoRR, 2021

Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence.

Dachao Lin, Haishan Ye, Zhihua Zhang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data.

Dachao Lin, Ruoyu Sun, Zhihua Zhang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Communication-Efficient Distributed SVD via Local Power Iterations.

Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang

Proceedings of the 38th International Conference on Machine Learning, 2021

Multi-split Reversible Transformers Can Enhance Neural Machine Translation.

Yuekai Zhao, Shuchang Zhou, Zhihua Zhang

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

2020

Nesterov's Acceleration for Approximate Newton.

Haishan Ye, Luo Luo, Zhihua Zhang

J. Mach. Learn. Res., 2020

Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs.

Wenhao Yang, Xiang Li, Guangzeng Xie, Zhihua Zhang

CoRR, 2020

Landscape of Sparse Linear Network: A Brief Investigation.

Dachao Lin, Ruoyu Sun, Zhihua Zhang

CoRR, 2020

Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond.

Dachao Lin, Peiqin Sun, Guangzeng Xie, Shuchang Zhou, Zhihua Zhang

CoRR, 2020

Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems.

Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang

Proceedings of the 37th International Conference on Machine Learning, 2020

On the Convergence of FedAvg on Non-IID Data.

Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang

Proceedings of the 8th International Conference on Learning Representations, 2020

Active Learning Approaches to Enhancing Neural Machine Translation: An Empirical Study.

Yuekai Zhao, Haoran Zhang, Shuchang Zhou, Zhihua Zhang

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Efficient Spectrum-Revealing CUR Matrix Decomposition.

Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Do Subsampled Newton Methods Work for High-Dimensional Data?

Xiang Li, Shusen Wang, Zhihua Zhang

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Fast stochastic second-order method logarithmic in condition number.

Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang

Pattern Recognit., 2019

Robust Frequent Directions with Application in Online Learning.

Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang

J. Mach. Learn. Res., 2019

Fast Generalized Matrix Regression with Applications in Machine Learning.

Haishan Ye, Shusen Wang, Zhihua Zhang, Tong Zhang

CoRR, 2019

Communication Efficient Decentralized Training with Multiple Local Updates.

Xiang Li, Wenhao Yang, Shusen Wang, Zhihua Zhang

CoRR, 2019

A Stochastic Proximal Point Algorithm for Saddle-Point Problems.

Luo Luo, Cheng Chen, Yujun Li, Guangzeng Xie, Zhihua Zhang

CoRR, 2019

A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization.

Guangzeng Xie, Luo Luo, Zhihua Zhang

CoRR, 2019

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks.

Hao Jin, Dachao Lin, Zhihua Zhang

CoRR, 2019

A Unified Framework for Regularized Reinforcement Learning.

Xiang Li, Wenhao Yang, Zhihua Zhang

CoRR, 2019

A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning.

Wenhao Yang, Xiang Li, Zhihua Zhang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Lipschitz Generative Adversarial Nets.

Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks.

Guangzeng Xie, Yitan Wang, Shuchang Zhou, Zhihua Zhang

CoRR, 2018

Sketched Follow-The-Regularized-Leader for Online Factorization Machine.

Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, Jian Pei

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017

Fast Fisher discriminant analysis with randomized algorithms.

Haishan Ye, Yujun Li, Cheng Chen, Zhihua Zhang

Pattern Recognit., 2017

Nesterov's Acceleration For Approximate Newton.

Haishan Ye, Zhihua Zhang

CoRR, 2017

A Unifying Framework for Convergence Analysis of Approximate Newton Methods.

Haishan Ye, Luo Luo, Zhihua Zhang

CoRR, 2017

Online Learning Via Regularized Frequent Directions.

Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li

CoRR, 2017

Approximate Newton Methods and Their Local Convergence.

Haishan Ye, Luo Luo, Zhihua Zhang

Proceedings of the 34th International Conference on Machine Learning, 2017

Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features.

Zihao Chen, Luo Luo, Zhihua Zhang

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition.

Shusen Wang, Zhihua Zhang, Tong Zhang

J. Mach. Learn. Res., 2016

SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions.

Shusen Wang, Luo Luo, Zhihua Zhang

J. Mach. Learn. Res., 2016

Tighter bound of Sketched Generalized Matrix Approximation.

Haishan Ye, Qiaoming Ye, Zhihua Zhang

CoRR, 2016

Revisiting Sub-sampled Newton Methods.

Haishan Ye, Luo Luo, Zhihua Zhang

CoRR, 2016

Variance-Reduced Second-Order Methods.

Luo Luo, Zihao Chen, Zhihua Zhang, Wu-Jun Li

CoRR, 2016

Quasi-Newton Hamiltonian Monte Carlo.

Tianfan Fu, Luo Luo, Zhihua Zhang

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA.

Qiaomin Ye, Luo Luo, Zhihua Zhang

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2015

Improved Analyses of the Randomized Power Method and Block Lanczos Method.

Shusen Wang, Zhihua Zhang, Tong Zhang

CoRR, 2015

Towards More Efficient Nystrom Approximation and CUR Matrix Decomposition.

Shusen Wang, Zhihua Zhang, Tong Zhang

CoRR, 2015

Distributed Multi-Armed Bandits: Regret vs. Communication.

Shuang Liu, Cheng Chen, Zhihua Zhang

CoRR, 2015

A Parallel algorithm for $\mathcal{X}$-Armed bandits.

Cheng Chen, Shuang Liu, Zhihua Zhang, Wu-Jun Li

CoRR, 2015

Support Matrix Machines.

Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Group Orbit Optimization: A Unified Approach to Data Normalization.

Shuchang Zhou, Zhihua Zhang, Xiaobing Feng

CoRR, 2014

Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion.

Shusen Wang, Tong Zhang, Zhihua Zhang

CoRR, 2014

The Modified Nystrom Method: Theories, Algorithms, and Extension.

Shusen Wang, Luo Luo, Zhihua Zhang

CoRR, 2014

Multicategory large margin classification methods: Hinge losses vs. coherence functions.

Zhihua Zhang, Cheng Chen, Guang Dai, Wu-Jun Li, Dit-Yan Yeung

Artif. Intell., 2014

2013

A Scalable Approach to Column-Based Low-Rank Matrix Approximation.

Yifan Pi, Haoruo Peng, Shuchang Zhou, Zhihua Zhang

Proceedings of the IJCAI 2013, 2013

2012

EP-GIG Priors and Applications in Bayesian Sparse Learning.

Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan

J. Mach. Learn. Res., 2012

An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling.

Zhihua Zhang, Dakan Wang, Edward Y. Chang

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Coherence functions with applications in large-margin classification methods.

Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan

J. Mach. Learn. Res., 2012

Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets.

Haoruo Peng, Zhengyu Wang, Edward Y. Chang, Shuchang Zhou, Zhihua Zhang

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

2011

Bayesian Generalized Kernel Mixed Models.

Zhihua Zhang, Guang Dai, Michael I. Jordan

J. Mach. Learn. Res., 2011

Generalized Latent Factor Models for Social Network Analysis.

Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang

Proceedings of the IJCAI 2011, 2011

2010

A regularization framework for multiclass classification: A deterministic annealing approach.

Zhihua Zhang, Gang Wang, Dit-Yan Yeung, Guang Dai, Frederick H. Lochovsky

Pattern Recognit., 2010

Regularized Discriminant Analysis, Ridge Regression and Beyond.

Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan

J. Mach. Learn. Res., 2010

Bayesian Generalized Kernel Models.

Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Matrix-Variate Dirichlet Process Mixture Models.

Zhihua Zhang, Guang Dai, Michael I. Jordan

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

2009

Coherence Functions for Multicategory Margin-based Classification Methods.

Zhihua Zhang, Michael I. Jordan, Wu-Jun Li, Dit-Yan Yeung

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Latent Variable Models for Dimensionality Reduction.

Zhihua Zhang, Michael I. Jordan

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Latent Wishart Processes for Relational Kernel Learning.

Wu-Jun Li, Zhihua Zhang, Dit-Yan Yeung

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis.

Zhihua Zhang, Guang Dai, Michael I. Jordan

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

Probabilistic Relational PCA.

Wu-Jun Li, Dit-Yan Yeung, Zhihua Zhang

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008

Posterior Consistency of the Silverman g-prior in Bayesian Model Choice.

Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

Semiparametric Regression Using Student t Processes.

Zhihua Zhang, Gang Wu, Edward Y. Chang

IEEE Trans. Neural Networks, 2007

Surrogate maximization/minimization algorithms and extensions.

Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

Mach. Learn., 2007

2006

Model-based transductive learning of the kernel matrix.

Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

Mach. Learn., 2006

Bayesian Multicategory Support Vector Machines.

Zhihua Zhang, Michael I. Jordan

Proceedings of the UAI '06, 2006

Adaptive non-linear clustering in data streams.

Ankur Jain, Zhihua Zhang, Edward Y. Chang

Proceedings of the 2006 ACM CIKM International Conference on Information and Knowledge Management, 2006

2005

Kronecker Factorization for Speeding up Kernel Machines.

Gang Wu, Zhihua Zhang, Edward Y. Chang

Proceedings of the 2005 SIAM International Conference on Data Mining, 2005

Learning with non-metric proximity matrices.

Gang Wu, Edward Y. Chang, Zhihua Zhang

Proceedings of the 13th ACM International Conference on Multimedia, 2005

A Bernoulli Relational Model for Nonlinear Embedding.

Gang Wang, Hui Zhang, Zhihua Zhang, Frederick H. Lochovsky

Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 2005

Annealed Discriminant Analysis.

Gang Wang, Zhihua Zhang, Frederick H. Lochovsky

Proceedings of the Machine Learning: ECML 2005, 2005

2004

Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm.

Zhihua Zhang, Dit-Yan Yeung, James T. Kwok

Proceedings of the Machine Learning, 2004

Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model.

Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

Proceedings of the Machine Learning, 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

Proceedings of the Nineteenth National Conference on Artificial Intelligence, 2004

2003

Parametric Distance Metric Learning with Label Information.

Zhihua Zhang, James T. Kwok, Dit-Yan Yeung

Proceedings of the IJCAI-03, 2003