Stochastic Spectral and Conjugate Descent Methods. - Dmitry Kovalev; Peter Richtárik; Eduard Gorbunov; Elnur Gasanov (NeurIPS, 2018)

On Primal and Dual Approaches for Distributed Stochastic Convex Optimization over Networks. - Darina Dvinskikh; Eduard Gorbunov; Alexander V. Gasnikov; Pavel E. Dvurechensky; César A. Uribe (CDC, 2019)

Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. - Alexander V. Gasnikov; Pavel E. Dvurechensky; Eduard Gorbunov; Evgeniya A. Vorontsova; Daniil Selikhanovych; César A. Uribe; Bo Jiang 007; Haoyue Wang; Shuzhong Zhang; Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; et al. (COLT, 2019)

Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization. - Alexander V. Gasnikov; Pavel E. Dvurechensky; Eduard Gorbunov; Evgeniya A. Vorontsova; Daniil Selikhanovych; César A. Uribe (COLT, 2019)

Accelerated Directional Search with Non-Euclidean Prox-Structure. - Evgeniya A. Vorontsova; Alexander V. Gasnikov; Eduard A. Gorbunov (Autom. Remote. Control., 2019)

Accelerated Gradient-Free Optimization Methods with a Non-Euclidean Proximal Operator. - Evgeniya A. Vorontsova; Alexander V. Gasnikov; Eduard A. Gorbunov; Pavel E. Dvurechenskii (Autom. Remote. Control., 2019)

Distributed Learning with Compressed Gradient Differences. - Konstantin Mishchenko; Eduard Gorbunov; Martin Takác 001; Peter Richtárik (CoRR, 2019)

A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. - Eduard Gorbunov; Filip Hanzely; Peter Richtárik (AISTATS, 2020)

A Stochastic Derivative Free Optimization Method with Momentum. - Eduard Gorbunov; Adel Bibi; Ozan Sener; El Houcine Bergou; Peter Richtárik (ICLR, 2020)

Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping. - Eduard Gorbunov; Marina Danilova; Alexander V. Gasnikov (NeurIPS, 2020)

Linearly Converging Error Compensated SGD. - Eduard Gorbunov; Dmitry Kovalev; Dmitry Makarenko; Peter Richtárik (NeurIPS, 2020)

Recent Theoretical Advances in Non-Convex Optimization. - Marina Danilova; Pavel E. Dvurechensky; Alexander V. Gasnikov; Eduard Gorbunov; Sergey Guminov; Dmitry Kamzolov; Innokentiy Shibaev (CoRR, 2020)

Stochastic Three Points Method for Unconstrained Smooth Minimization. - El Houcine Bergou; Eduard Gorbunov; Peter Richtárik (SIAM J. Optim., 2020)

Local SGD: Unified Theory and New Efficient Methods. - Eduard Gorbunov; Filip Hanzely; Peter Richtárik (AISTATS, 2021)

MARINA: Faster Non-Convex Distributed Learning with Compression. - Eduard Gorbunov; Konstantin Burlachenko; Zhize Li; Peter Richtárik (ICML, 2021)

Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices. - Max Ryabinin; Eduard Gorbunov; Vsevolod Plokhotnyuk; Gennady Pekhimenko (NeurIPS, 2021)

Near-Optimal High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise. - Eduard Gorbunov; Marina Danilova; Innokentiy Shibaev; Pavel E. Dvurechensky; Alexander V. Gasnikov (CoRR, 2021)

EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback. - Ilyas Fatkhullin; Igor Sokolov 001; Eduard Gorbunov; Zhize Li; Peter Richtárik (CoRR, 2021)

Distributed and Stochastic Optimization Methods with Gradient Compression and Local Steps. - Eduard Gorbunov (CoRR, 2021)

An accelerated directional derivative method for smooth stochastic convex optimization. - Pavel E. Dvurechensky; Eduard Gorbunov; Alexander V. Gasnikov (Eur. J. Oper. Res., 2021)

Stochastic Extragradient: General Analysis and Improved Rates. - Eduard Gorbunov; Hugo Berard; Gauthier Gidel; Nicolas Loizou (AISTATS, 2022)

Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. - Eduard Gorbunov; Nicolas Loizou; Gauthier Gidel (AISTATS, 2022)

Secure Distributed Training at Scale. - Eduard Gorbunov; Alexander Borzunov; Michael Diskin; Max Ryabinin (ICML, 2022)

3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. - Peter Richtárik; Igor Sokolov 001; Elnur Gasanov; Ilyas Fatkhullin; Zhize Li; Eduard Gorbunov (ICML, 2022)

Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise. - Eduard Gorbunov; Marina Danilova; David Dobre; Pavel E. Dvurechenskii; Alexander V. Gasnikov; Gauthier Gidel (NeurIPS, 2022)

Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities. - Eduard Gorbunov; Adrien B. Taylor; Gauthier Gidel (NeurIPS, 2022)

Distributed Methods with Absolute Compression and Error Compensation. - Marina Danilova; Eduard Gorbunov (CoRR, 2022)

Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. - Abdurakhmon Sadiev; Grigory Malinovsky; Eduard Gorbunov; Igor Sokolov 001; Ahmed Khaled 001; Konstantin Burlachenko; Peter Richtárik (CoRR, 2022)

Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems - Survey. - Aleksandr Beznosikov; Boris T. Polyak; Eduard Gorbunov; Dmitry Kovalev; Alexander V. Gasnikov (CoRR, 2022)

An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization. - Eduard Gorbunov; Pavel E. Dvurechensky; Alexander V. Gasnikov (SIAM J. Optim., 2022)

Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. - Aleksandr Beznosikov; Eduard Gorbunov; Hugo Berard; Nicolas Loizou (AISTATS, 2023)

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)

Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity. - Eduard Gorbunov; Adrien B. Taylor; Samuel Horváth; Gauthier Gidel (ICML, 2023)

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 E. Dvurechensky; Alexander V. Gasnikov; Peter Richtárik (ICML, 2023)

Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions. - Sayantan Choudhury; Eduard Gorbunov; Nicolas Loizou (NeurIPS, 2023)

Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance. - Nikita Kornilov; Ohad Shamir; Aleksandr V. Lobanov; Darina Dvinskikh; Alexander V. Gasnikov; Innokentiy Shibaev; Eduard Gorbunov; Samuel Horváth (NeurIPS, 2023)

Byzantine-Tolerant Methods for Distributed Variational Inequalities. - Nazarii Tupitsa; Abdulla Jasem Almansoori; Yanlin Wu; Martin Takác 001; Karthik Nandakumar; Samuel Horváth; Eduard Gorbunov (NeurIPS, 2023)

Byzantine-Robust Loopless Stochastic Variance-Reduced Gradient. - Nikita Fedin; Eduard Gorbunov (CoRR, 2023)

Unified analysis of SGD-type methods. - Eduard Gorbunov (CoRR, 2023)

Clip21: Error Feedback for Gradient Clipping. - Sarit Khirirat; Eduard Gorbunov; Samuel Horváth; Rustem Islamov; Fakhri Karray; Peter Richtárik (CoRR, 2023)

Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. - Grigory Malinovsky; Peter Richtárik; Samuel Horváth; Eduard Gorbunov (CoRR, 2023)

Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems. - Nikita Puchkin; Eduard Gorbunov; Nikolay Kutuzov; Alexander V. Gasnikov (AISTATS, 2024)

Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates. - Ahmad Rammal; Kaja Gruntkowska; Nikita Fedin; Eduard Gorbunov; Peter Richtárik (AISTATS, 2024)

Low-Resource Machine Translation through the Lens of Personalized Federated Learning. - Viktor Moskvoretskii; Nazarii Tupitsa; Chris Biemann; Samuel Horváth; Eduard Gorbunov; Irina Nikishina (EMNLP (Findings), 2024)

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 E. Dvurechensky; Alexander V. Gasnikov; Peter Richtárik (ICML, 2024)

Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations. - Artem Agafonov; Petr Ostroukhov; Roman Mozhaev; Konstantin Yakovlev; Eduard Gorbunov; Martin Takác 001; Alexander V. Gasnikov; Dmitry Kamzolov (NeurIPS, 2024)

Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. - Sayantan Choudhury; Nazarii Tupitsa; Nicolas Loizou; Samuel Horváth; Martin Takác 001; Eduard Gorbunov (NeurIPS, 2024)

Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences. - Grigory Malinovsky; Peter Richtárik; Samuel Horváth; Eduard Gorbunov (NeurIPS, 2024)

Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences. - Abdurakhmon Sadiev; Grigory Malinovsky; Eduard Gorbunov; Igor Sokolov 001; Ahmed Khaled 001; Konstantin Burlachenko; Peter Richtárik (NeurIPS, 2024)

Federated Learning Can Find Friends That Are Beneficial. - Nazarii Tupitsa; Samuel Horváth; Martin Takác 001; Eduard Gorbunov (CoRR, 2024)

Gradient Clipping Improves AdaGrad when the Noise Is Heavy-Tailed. - Saveliy Chezhegov; Yaroslav Klyukin; Andrei Semenov; Aleksandr Beznosikov; Alexander V. Gasnikov; Samuel Horváth; Martin Takác 001; Eduard Gorbunov (CoRR, 2024)

Methods for Convex (L0,L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity. - Eduard Gorbunov; Nazarii Tupitsa; Sayantan Choudhury; Alen Aliev; Peter Richtárik; Samuel Horváth; Martin Takác 001 (CoRR, 2024)

Error Feedback under (L0,L1)-Smoothness: Normalization and Momentum. - Sarit Khirirat; Abdurakhmon Sadiev; Artem Riabinin; Eduard Gorbunov; Peter Richtárik (CoRR, 2024)

Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning. - Kaustubh Ponkshe; Raghav Singhal; Eduard Gorbunov; Alexey Tumanov; Samuel Horváth; Praneeth Vepakomma (CoRR, 2024)

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 V. Gasnikov (Comput. Manag. Sci., 2024)

High-Probability Complexity Bounds for Non-smooth Stochastic Convex Optimization with Heavy-Tailed Noise. - Eduard Gorbunov; Marina Danilova; Innokentiy Shibaev; Pavel E. Dvurechensky; Alexander V. Gasnikov (J. Optim. Theory Appl., 2024)

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. - Yury Demidovich; Petr Ostroukhov; Grigory Malinovsky; Samuel Horváth; Martin Takác 001; Peter Richtárik; Eduard Gorbunov (ICLR, 2025)

Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity. - Eduard Gorbunov; Nazarii Tupitsa; Sayantan Choudhury; Alen Aliev; Peter Richtárik; Samuel Horváth; Martin Takác 001 (ICLR, 2025)

Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity. - Konstantin Mishchenko; Rustem Islamov; Eduard Gorbunov; Samuel Horváth (Trans. Mach. Learn. Res., 2025)

Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy. - Rustem Islamov; Samuel Horváth; Aurélien Lucchi; Peter Richtárik; Eduard Gorbunov (CoRR, 2025)

Convergence of Clipped-SGD for Convex (L0,L1)-Smooth Optimization with Heavy-Tailed Noise. - Saveliy Chezhegov; Aleksandr Beznosikov; Samuel Horváth; Eduard Gorbunov (CoRR, 2025)

On the Interaction of Noise, Compression Role, and Adaptivity under (L0, L1)-Smoothness: An SDE-based Approach. - Enea Monzio Compagnoni; Rustem Islamov; Antonio Orvieto; Eduard Gorbunov (CoRR, 2025)

Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level. - Saleh Vatan Khah; Savelii Chezhegov; Shahrokh Farahmand; Samuel Horváth; Eduard Gorbunov (CoRR, 2025)

Intermediate Gradient Methods with Relative Inexactness. - Nikita Kornilov; Mohammad S. Alkousa; Eduard Gorbunov; Fedor S. Stonyakin; Pavel E. Dvurechensky; Alexander V. Gasnikov (J. Optim. Theory Appl., 2025)