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ORCIDEgor Shulgin
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2020 – today
- 2024
[c7]Egor Shulgin, Peter Richtárik:
Towards a Better Theoretical Understanding of Independent Subnetwork Training. ICML 2024- [i9]Avetik G. Karagulyan, Egor Shulgin, Abdurakhmon Sadiev, Peter Richtárik:
SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning. CoRR abs/2405.20127 (2024) - 2023
- [i8]Egor Shulgin, Peter Richtárik:
Towards a Better Theoretical Understanding of Independent Subnetwork Training. CoRR abs/2306.16484 (2023) - [i7]Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik:
MAST: Model-Agnostic Sparsified Training. CoRR abs/2311.16086 (2023) - 2022
- [c6]Egor Shulgin, Peter Richtárik:
Shifted compression framework: generalizations and improvements. UAI 2022: 1813-1823 - [i6]Motasem Alfarra, Juan C. Pérez, Egor Shulgin, Peter Richtárik, Bernard Ghanem:
Certified Robustness in Federated Learning. CoRR abs/2206.02535 (2022) - [i5]Egor Shulgin, Peter Richtárik:
Shifted Compression Framework: Generalizations and Improvements. CoRR abs/2206.10452 (2022) - 2021
- [c5]Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov:
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. ICML 2021: 5784-5793 - [c4]Anastasiya Ivanova, Dmitry Pasechnyuk, Dmitry Grishchenko, Egor Shulgin, Alexander V. Gasnikov, Vladislav Matyukhin:
Adaptive Catalyst for Smooth Convex Optimization. OPTIMA 2021: 20-37 - [c3]Alexander Rogozin, Vladislav Lukoshkin, Alexander V. Gasnikov, Dmitry Kovalev
, Egor Shulgin:
Towards Accelerated Rates for Distributed Optimization over Time-Varying Networks. OPTIMA 2021: 258-272 - [i4]Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov:
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. CoRR abs/2102.09234 (2021) - 2020
- [c2]Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky:
Revisiting Stochastic Extragradient. AISTATS 2020: 4573-4582 - [i3]Mher Safaryan, Egor Shulgin, Peter Richtárik:
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor. CoRR abs/2002.08958 (2020)
2010 – 2019
- 2019
- [c1]Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin:
SGD with Arbitrary Sampling: General Analysis and Improved Rates. ICML 2019: 5200-5209 - [i2]Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik:
SGD: General Analysis and Improved Rates. CoRR abs/1901.09401 (2019) - [i1]Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky:
Revisiting Stochastic Extragradient. CoRR abs/1905.11373 (2019)
Coauthor Index
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