Publications

Egor Shulgin, Peter Richtárik. Shifted Compression Framework: Generalizations and Improvements. Uncertainty in Artificial Intelligence (UAI 2022), 2022.

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Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander Gasnikov. ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. preprint arXiv:2102.09234, 2021.

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Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky. Revisiting Stochastic Extragradient. The 23rd International Conference on Artificial Intelligence and Statistics, 2020.

PDF Cite Video arXiv AISTATS 2020 Proceedings NeurIPS 2019 Workshop

Mher Safaryan, Egor Shulgin, Peter Richtárik. Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor. preprint arXiv:2002.08958, 2020.

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Anastasiya Ivanova, Dmitry Pasechnyuk, Dmitry Grishchenko, Egor Shulgin, Alexander Gasnikov, Vladislav Matyukhin. Adaptive Catalyst for Smooth Convex Optimization. preprint arXiv:1911.11271, 2020.

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Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik. SGD: General Analysis and Improved Rates. Proceedings of the 36th International Conference on Machine Learning, 2019.

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Alexander Gasnikov, Eduard Gorbunov, Sergey Guz, Elena Chernousova, Maksim Shirobokov, Egor Shulgin. Lecture Notes on Stochastic Processes. Book in Russian, 2019.

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