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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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Communication-Efficient Distributed SVD via Local Power Iterations


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


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Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction


Dec 29, 2019
Songgaojun Deng, Shusen Wang, Huzefa Rangwala, Lijing Wang, Yue Ning

* 17 pages, 22 figures, 5 tables 

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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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Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping


Sep 24, 2019
Shusen Wang


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Matrix Sketching for Secure Collaborative Machine Learning


Sep 24, 2019
Shusen Wang


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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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Do Subsampled Newton Methods Work for High-Dimensional Data?


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


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GIANT: Globally Improved Approximate Newton Method for Distributed Optimization


Sep 11, 2018
Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney

* Fixed some typos. Improved writing 

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Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap


Sep 06, 2018
Miles E. Lopes, Shusen Wang, Michael W. Mahoney


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Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging


May 05, 2018
Shusen Wang, Alex Gittens, Michael W. Mahoney

* Journal of Machine Learning Research, 19, pp1-50, 2018 
* To appear in Journal of Machine Learning Research, 2018. A short version has appeared in International Conference on Machine Learning (ICML), 2017 

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Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm


Oct 11, 2017
Youzuo Lin, Shusen Wang, Jayaraman Thiagarajan, George Guthrie, David Coblentz


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A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication


Aug 06, 2017
Miles E. Lopes, Shusen Wang, Michael W. Mahoney


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Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds


Jun 24, 2017
Shusen Wang, Alex Gittens, Michael W. Mahoney

* Changes: 1. more large-scale experiment results; 2. power method for kernel k-means; 3. compare with Musco & Musco's work 

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Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition


Dec 10, 2016
Shusen Wang, Zhihua Zhang, Tong Zhang

* Journal of Machine Learning Research 2016 

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


May 20, 2016
Shusen Wang, Luo Luo, Zhihua Zhang

* Journal of Machine Learning Research, 2016 

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A Practical Guide to Randomized Matrix Computations with MATLAB Implementations


Nov 03, 2015
Shusen Wang


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


Feb 10, 2015
Shusen Wang, Tong Zhang, Zhihua Zhang


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Sharpened Error Bounds for Random Sampling Based $\ell_2$ Regression


Apr 05, 2014
Shusen Wang

* unpublished manuscript 

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Efficient Algorithms and Error Analysis for the Modified Nystrom Method


Apr 01, 2014
Shusen Wang, Zhihua Zhang

* 9-page paper plus appendix. In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS) 2014, Reykjavik, Iceland. JMLR: W&CP volume 33 

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Improving CUR Matrix Decomposition and the Nyström Approximation via Adaptive Sampling


Oct 01, 2013
Shusen Wang, Zhihua Zhang

* Journal of Machine Learning Research, 14: 2549-2589, 2013 

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A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound


Oct 04, 2012
Shusen Wang, Zhihua Zhang, Jian Li

* Shusen Wang and Zhihua Zhang. A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound. In Advances in Neural Information Processing Systems 25, 2012 
* accepted by NIPS 2012 

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EP-GIG Priors and Applications in Bayesian Sparse Learning


Apr 19, 2012
Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan

* 33 pages, 10 figures 

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