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Peter Richtarik

Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition

Jun 01, 2022
Lukang Sun, Avetik Karagulyan, Peter Richtarik

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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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Optimal Client Sampling for Federated Learning

Oct 26, 2020
Wenlin Chen, Samuel Horvath, Peter Richtarik

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* 11 pages, 10 pages of Appendix, 6 Figures, 3 algorithms, code available: https://github.com/SamuelHorvath/FL-optimal-client-sampling

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Variance-Reduced Methods for Machine Learning

Oct 02, 2020
Robert M. Gower, Mark Schmidt, Francis Bach, Peter Richtarik

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* 16 pages, 7 figures, 1 table

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Adaptive Learning of the Optimal Mini-Batch Size of SGD

May 03, 2020
Motasem Alfarra, Slavomir Hanzely, Alyazeed Albasyoni, Bernard Ghanem, Peter Richtarik

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* 17 pages, 45 figures

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Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems

Feb 11, 2020
Filip Hanzely, Dmitry Kovalev, Peter Richtarik

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* 30 pages, 8 figures

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