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Shanshan Wu

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

Jun 18, 2022
Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ziyu Liu, Zheng Xu, Virginia Smith

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* 35 pages, 9 figures, 5 tables. Code: https://github.com/google-research/federated/tree/master/personalization_benchmark

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A Field Guide to Federated Optimization

Jul 14, 2021
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu

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Learning Distributions Generated by One-Layer ReLU Networks

Sep 19, 2019
Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi

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* NeurIPS 2019

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Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models

Oct 28, 2018
Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis

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* 29 pages, 3 figures

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The Sparse Recovery Autoencoder

Jul 05, 2018
Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

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* 23 pages, 8 figures

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