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Statistical Estimation and Inference via Local SGD in Federated Learning


Sep 03, 2021
Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang


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


Jun 03, 2021
Luo Luo, Guangzeng Xie, Tong Zhang, Zhihua Zhang


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Memory-Efficient Differentiable Transformer Architecture Search


May 31, 2021
Yuekai Zhao, Li Dong, Yelong Shen, Zhihua Zhang, Furu Wei, Weizhu Chen

* Accepted by Findings of ACL 2021 

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Directional Convergence Analysis under Spherically Symmetric Distribution


May 09, 2021
Dachao Lin, Zhihua Zhang


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Non-asymptotic Performances of Robust Markov Decision Processes


May 09, 2021
Wenhao Yang, Zhihua Zhang


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


Apr 12, 2021
Guangzeng Xie, Hao Jin, Dachao Lin, Zhihua Zhang


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Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction


Mar 22, 2021
Yuze Han, Guangzeng Xie, Zhihua Zhang


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DIPPA: An improved Method for Bilinear Saddle Point Problems


Mar 15, 2021
Guangzeng Xie, Yuze Han, Zhihua Zhang


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Privacy-Preserving Distributed SVD via Federated Power


Mar 01, 2021
Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang, Zhihua Zhang


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


Jan 05, 2021
Xiang Li, Zhihua Zhang


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Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs


Oct 31, 2020
Wenhao Yang, Xiang Li, Guangzeng Xie, Zhihua Zhang


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Landscape of Sparse Linear Network: A Brief Investigation


Sep 16, 2020
Dachao Lin, Ruoyu Sun, Zhihua Zhang


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Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond


Aug 30, 2020
Dachao Lin, Peiqin Sun, Guangzeng Xie, Shuchang Zhou, Zhihua Zhang


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Intervention Generative Adversarial Networks


Aug 09, 2020
Jiadong Liang, Liangyu Zhang, Cheng Zhang, Zhihua Zhang


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An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization


Jul 11, 2020
Yimin Huang, Yujun Li, Zhenguo Li, Zhihua Zhang


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Communication-Efficient Distributed SVD via Local Power Iterations


Feb 19, 2020
Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang


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Fast Generalized Matrix Regression with Applications in Machine Learning


Dec 27, 2019
Haishan Ye, Shusen Wang, Zhihua Zhang, Tong Zhang


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Communication Efficient Decentralized Training with Multiple Local Updates


Oct 28, 2019
Xiang Li, Wenhao Yang, Shusen Wang, Zhihua Zhang


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Distillation $\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network


Oct 02, 2019
Bin Dong, Jikai Hou, Yiping Lu, Zhihua Zhang

* Accepted by NeurIPS 2019 Workshop on Machine Learning with Guarantees. Submitted to other places 

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A Stochastic Proximal Point Algorithm for Saddle-Point Problems


Sep 13, 2019
Luo Luo, Cheng Chen, Yujun Li, Guangzeng Xie, Zhihua Zhang


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


Aug 22, 2019
Guangzeng Xie, Luo Luo, Zhihua Zhang


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


Aug 18, 2019
Hao Jin, Dachao Lin, Zhihua Zhang


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On the Convergence of FedAvg on Non-IID Data


Jul 04, 2019
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, Zhihua Zhang


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A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems


May 28, 2019
Tianle Cai, Ruiqi Gao, Jikai Hou, Siyu Chen, Dong Wang, Di He, Zhihua Zhang, Liwei Wang

* Submitted to NeurIPS 2019 

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Lipschitz Generative Adversarial Nets


Mar 14, 2019
Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang

* Under review by the International Conference on Machine Learning (ICML 2019) 

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A Unified Framework for Regularized Reinforcement Learning


Mar 02, 2019
Xiang Li, Wenhao Yang, Zhihua Zhang


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Do Subsampled Newton Methods Work for High-Dimensional Data?


Feb 13, 2019
Xiang Li, Shusen Wang, Zhihua Zhang


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