Prepared in 2024

[52] Federated Learning Can Find Friends That Are Beneficial
Nazarii Tupitsa, Samuel Horváth, Martin Takáč, Eduard Gorbunov
February 2024

[51] Zeroth-order Median Clipping for Non-Smooth Convex Optimization Problems with Heavy-tailed Symmetric Noise
Nikita Kornilov, Yuriy Dorn, Aleksandr Lobanov, Nikolay Kutuzov, Innokentiy Shibaev, Eduard Gorbunov, Alexander Gasnikov, Alexander Nazin
February 2024

Prepared in 2023

[50] Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences
Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov
November 2023

[49] Byzantine-Tolerant Methods for Distributed Variational Inequalities
Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takáč, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov
NeurIPS 2023
November 2023

[48] Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
Nikita Puchkin*, Eduard Gorbunov*, Nikolay Kutuzov, Alexander Gasnikov
*equal contribution
November 2023

[47] Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
Nikita Kornilov, Ohad Shamir, Aleksandr Lobanov, Darina Dvinskikh, Alexander Gasnikov, Innokentiy Shibaev,
Eduard Gorbunov, Samuel Horváth
NeurIPS 2023
October 2023

[46] Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtárik
October 2023

[45] High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel Dvurechensky,
Alexander Gasnikov, Peter Richtárik
October 2023

[44] Intermediate Gradient Methods with Relative Inexactness
Nikita Kornilov, Eduard Gorbunov, Mohammad Alkousa, Fedor Stonyakin, Pavel Dvurechensky, Alexander Gasnikov
October 2023

[43] Clip21: Error Feedback for Gradient Clipping
Sarit Khirirat, Eduard Gorbunov, Samuel Horváth, Rustem Islamov, Fakhri Karray, Peter Richtárik
May 2023

[42] Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity
Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov, Samuel Horváth
May 2023

[41] Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits
Yuriy Dorn, Nikita Kornilov, Nikolay Kutuzov, Alexander Nazin, Eduard Gorbunov, Alexander Gasnikov
May 2023

[40] Unified analysis of SGD-type methods
Eduard Gorbunov
March 2023

[39] Byzantine-Robust Loopless Stochastic Variance-Reduced Gradient
Nikita Fedin, Eduard Gorbunov
MOTOR 2023
March 2023

[38] Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions
Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou 
NeurIPS 2023
February 2023

[37] High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance  
Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel Dvurechensky,
Alexander Gasnikov, Peter Richtárik
ICML 2023
February 2023

Prepared in 2022

[36] Randomized gradient-free methods in convex optimization 
Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky, Eduard Gorbunov, Aleksander Beznosikov, Alexander Lobanov
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
ICML 2023
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
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
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
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
June 2022

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

[29] Distributed Methods with Absolute Compression and Error Compensation
Marina Danilova, Eduard Gorbunov
MOTOR 2022
March 2022

[28] Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
Aleksandr Beznosikov*, Eduard Gorbunov*, Hugo Berard*, Nicolas Loizou
(*equal contribution)
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
ICML 2022
February 2022

Prepared in 2021

[26] Stochastic Extragradient: General Analysis and Improved Rates 
Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou
November 2021

[25] Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities
and Connections With Cocoercivity
Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel
October 2021

[24] EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback
Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtárik
Short version of this work was accepted to the NeurIPS 2021 workshop OPT2021  
October 2021

[23] Secure Distributed Training at Scale
Eduard Gorbunov*, Alexander Borzunov*, Michael Diskin, Max Ryabinin
(*equal contribution)
ICML 2022
June 2021

[22] Near-Optimal High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
Eduard Gorbunov, Marina Danilova, Innokentiy Shibaev, Pavel Dvurechensky, Alexander Gasnikov
June 2021

[21] Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
Max Ryabinin*, Eduard Gorbunov*, Vsevolod Plokhotnyuk, Gennady Pekhimenko
(*equal contribution)
NeurIPS 2021
March 2021

[20] MARINA: Faster Non-Convex Distributed Learning with Compression
Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik
ICML 2021
February 2021
[poster ICML 2021]

Prepared in 2020

[19] Recent Theoretical Advances in Non-Convex Optimization
Marina Danilova, Pavel Dvurechensky, Alexander Gasnikov, Eduard Gorbunov, Sergey Guminov,
Dmitry Kamzolov, Innokentiy Shibaev
High-Dimensional Optimization and Probability: With a View Towards Data Science
December 2020

[18] Recent theoretical advances in decentralized distributed convex optimization
Eduard Gorbunov, Alexander Rogozin, Aleksandr Beznosikov, Darina Dvinskikh, Alexander Gasnikov
High-Dimensional Optimization and Probability: With a View Towards Data Science
November 2020

[17] Local SGD: Unified Theory and New Efficient Methods
Eduard Gorbunov, Filip Hanzely and Peter Richtárik
November 2020
[poster AISTATS 2021]

[16] Linearly Converging Error Compensated SGD
Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko and Peter Richtárik
NeurIPS 2020
October 2020
[video MLSS 2020] [slides MLSS 2020] [video FLOW] [slides FLOW] [poster NeurIPS 2020] [video NeurIPS 2020]

[15] Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
Eduard Gorbunov, Marina Danilova and Alexander Gasnikov
NeurIPS 2020
arXiv: 2005.10785
May 2020
[poster NeurIPS 2020] [video NeurIPS 2020]

Prepared in 2019

[14] Derivative-Free Method For Decentralized Distributed Non-Smooth Optimization
Aleksandr Beznosikov, Eduard Gorbunov and Alexander Gasnikov
IFAC-PapersOnLine, Volume 53, Issue 2, 2020, Pages 4038-4043
arXiv: 1911.10645
November 2019

[13] Optimal Decentralized Distributed Algorithms for Stochastic Convex Optimization
Eduard Gorbunov, Darina Dvinskikh and Alexander Gasnikov
arXiv: 1911.07363
November 2019

[12] Accelerated Gradient-Free Optimization Methods with a Non-Euclidean Proximal Operator
E. Vorontsova, A. Gasnikov, E Gorbunov, P. Dvurechensky
Automation and Remote Control, August 2019, Volume 80, Issue 8, pp 1487–1501

[11] A Stochastic Derivative Free Optimization Method with Momentum
Eduard Gorbunov, Adel Bibi, Ozan Sener, El Houcine Bergou and Peter Richtárik
ICLR 2020
arXiv: 1905.13278
May 2019

[10] A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent
Eduard Gorbunov, Filip Hanzely and Peter Richtárik
arXiv: 1905.11261
May 2019

[9] On Primal-Dual Approach for Distributed Stochastic Convex Optimization over Networks
Darina Dvinskikh, Eduard Gorbunov, Alexander Gasnikov, Pavel Dvurechensky, Cesar A. Uribe
58th Conference on Decision and Control (CDC 2019)
arXiv: 1903.09844
March 2019

[8] Stochastic Three Points Method for Unconstrained Smooth Minimization
El Houcine Bergou, Eduard Gorbunov and Peter Richtárik
SIAM Journal on Optimization 30, no. 4 (2020): 2726-2749
arXiv: 1902.03591
February 2019

[7] Distributed learning with compressed gradient differences
Konstantin Mishchenko, Eduard Gorbunov, Martin Takáč and Peter Richtárik
arXiv: 1901.09269
January 2019

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