Egor Shulgin

According to our database, Egor Shulgin authored at least 12 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning.

Avetik G. Karagulyan, Egor Shulgin, Abdurakhmon Sadiev, Peter Richtárik

CoRR, 2024

Towards a Better Theoretical Understanding of Independent Subnetwork Training.

Egor Shulgin, Peter Richtárik

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

MAST: Model-Agnostic Sparsified Training.

Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik

CoRR, 2023

2022

Certified Robustness in Federated Learning.

Motasem Alfarra, Juan C. Pérez, Egor Shulgin, Peter Richtárik, Bernard Ghanem

CoRR, 2022

Shifted compression framework: generalizations and improvements.

Egor Shulgin, Peter Richtárik

Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021

Towards Accelerated Rates for Distributed Optimization over Time-Varying Networks.

Alexander Rogozin, Vladislav Lukoshkin, Alexander V. Gasnikov, Dmitry Kovalev, Egor Shulgin

Proceedings of the Optimization and Applications - 12th International Conference, 2021

Adaptive Catalyst for Smooth Convex Optimization.

Anastasiya Ivanova, Dmitry Pasechnyuk, Dmitry Grishchenko, Egor Shulgin, Alexander V. Gasnikov, Vladislav Matyukhin

Proceedings of the Optimization and Applications - 12th International Conference, 2021

ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks.

Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor.

Mher Safaryan, Egor Shulgin, Peter Richtárik

CoRR, 2020

Revisiting Stochastic Extragradient.

Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

SGD: General Analysis and Improved Rates.

Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik

CoRR, 2019

SGD with Arbitrary Sampling: General Analysis and Improved Rates.

Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin

Proceedings of the 36th International Conference on Machine Learning, 2019