Prepared in 2022

[36] Randomized gradient-free methods in convex optimization 
Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky, Eduard Gorbunov, Aleksander Beznosikov, Alexander Lobanov
arXiv:2211.13566
November 2022

[35] Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity:
the Case of Negative Comonotonicity
Eduard Gorbunov, Adrien Taylor, Samuel Horváth, Gauthier Gidel
arXiv:2210.13831
October 2022

[34] Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems - Survey
Aleksandr Beznosikov, Boris Polyak, Eduard Gorbunov, Dmitry Kovalev, Alexander Gasnikov
European Mathematical Society Magazine, (127), 15-28
arXiv:2208.13592
August 2022

[33] Federated Optimization Algorithms with Random Reshuffling and Gradient Compression
Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled,
Konstantin Burlachenko, Peter Richtárik
arXiv:2206.07021
June 2022

[32]  Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov*, Marina Danilova*, David Dobre*, Pavel Dvurechensky, Alexander Gasnikov, Gauthier Gidel
(*equal contribution)
NeurIPS 2022
arXiv:2206.01095
June 2022

[31] Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions
and Communication Compression as a Cherry on the Top
Eduard Gorbunov, Samuel Horváth, Peter Richtárik, Gauthier Gidel
ICLR 2023
arXiv:2206.00529 
June 2022

[30] Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities
Eduard Gorbunov, Adrien Taylor, Gauthier Gidel
NeurIPS 2022
arXiv:2205.08446
May 2022

[29] Distributed Methods with Absolute Compression and Error Compensation
Marina Danilova, Eduard Gorbunov
Accepted to MOTOR 2022
arXiv:2203.02383
March 2022

[28] Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
Aleksandr Beznosikov*, Eduard Gorbunov*, Hugo Berard*, Nicolas Loizou
(*equal contribution)
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:172-235
(AISTATS 2023)
arXiv:2202.07262
February 2022

[27] 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory
for Lazy Aggregation
Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin, Elnur Gasanov, Zhize Li, Eduard Gorbunov
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18596-18648 (ICML 2022)
arXiv:2202.00998
February 2022