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Sai Praneeth Karimireddy

RelaySum for Decentralized Deep Learning on Heterogeneous Data


Oct 08, 2021
Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi

* To appear in NeurIPS 2021 

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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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Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data


Feb 09, 2021
Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi


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Learning from History for Byzantine Robust Optimization


Dec 18, 2020
Sai Praneeth Karimireddy, Lie He, Martin Jaggi

* 22 pages, 5 figures 

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Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning


Aug 08, 2020
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh


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PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning


Aug 04, 2020
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi


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Byzantine-Robust Learning on Heterogeneous Datasets via Resampling


Jun 23, 2020
Lie He, Sai Praneeth Karimireddy, Martin Jaggi


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Secure Byzantine-Robust Machine Learning


Jun 08, 2020
Lie He, Sai Praneeth Karimireddy, Martin Jaggi


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Why ADAM Beats SGD for Attention Models


Dec 06, 2019
Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J Reddi, Sanjiv Kumar, Suvrit Sra


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SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning


Oct 14, 2019
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh


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The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication


Sep 11, 2019
Sebastian U. Stich, Sai Praneeth Karimireddy


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Amplifying Rényi Differential Privacy via Shuffling


Jul 26, 2019
Eloïse Berthier, Sai Praneeth Karimireddy

* This version (and v1) have incorrect proofs! We are currently working on fixing these 

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PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization


May 31, 2019
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi


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Accelerating Gradient Boosting Machine


Mar 20, 2019
Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab Mirrokni


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Error Feedback Fixes SignSGD and other Gradient Compression Schemes


Jan 28, 2019
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi


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Efficient Greedy Coordinate Descent for Composite Problems


Oct 16, 2018
Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

* 44 pages, 17 figures, 3 tables 

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On Matching Pursuit and Coordinate Descent


Jul 02, 2018
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi

* ICML 2018 - Proceedings of the 35th International Conference on Machine Learning 

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Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients


Jun 01, 2018
Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi

* 19 pages 

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