Eduard Gorbunov
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
21
Venues
5
Active years
2019–2025
Best venue rank
A*
Where they publish
Papers
21 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. | Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky, Samuel Horvth, Martin Takc, Peter Richtrik, Eduard Gorbunov |
| 2025 | ICLR | Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity. | Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury, Alen Aliev, Peter Richtrik, Samuel Horvth, Martin Takc |
| 2025 | ICML | Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed. | Savelii Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander V. Gasnikov, Samuel Horvth, Martin Takc, Eduard Gorbunov |
| 2024 | AISTATS | Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems. | Nikita Puchkin, Eduard Gorbunov, Nikolay Kutuzov, Alexander V. Gasnikov |
| 2024 | AISTATS | Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates. | Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtrik |
| 2024 | EMNLP | Low-Resource Machine Translation through the Lens of Personalized Federated Learning. | Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann, Samuel Horvth, Eduard Gorbunov, Irina Nikishina |
| 2024 | ICML | High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise. | Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horvth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtrik |
| 2023 | AISTATS | Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. | Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou |
| 2023 | ICLR | Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. | Eduard Gorbunov, Samuel Horvth, Peter Richtrik, Gauthier Gidel |
| 2023 | ICML | Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity. | Eduard Gorbunov, Adrien B. Taylor, Samuel Horvth, Gauthier Gidel |
| 2023 | ICML | High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. | Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horvth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtrik |
| 2022 | AISTATS | Stochastic Extragradient: General Analysis and Improved Rates. | Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou |
| 2022 | AISTATS | Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. | Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel |
| 2022 | ICML | Secure Distributed Training at Scale. | Eduard Gorbunov, Alexander Borzunov, Michael Diskin, Max Ryabinin |
| 2022 | ICML | 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. | Peter Richtrik, Igor Sokolov, Elnur Gasanov, Ilyas Fatkhullin, Zhize Li, Eduard Gorbunov |
| 2021 | AISTATS | Local SGD: Unified Theory and New Efficient Methods. | Eduard Gorbunov, Filip Hanzely, Peter Richtrik |
| 2021 | ICML | MARINA: Faster Non-Convex Distributed Learning with Compression. | Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtrik |
| 2020 | AISTATS | A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. | Eduard Gorbunov, Filip Hanzely, Peter Richtrik |
| 2020 | ICLR | A Stochastic Derivative Free Optimization Method with Momentum. | Eduard Gorbunov, Adel Bibi, Ozan Sener, El Houcine Bergou, Peter Richtrik |
| 2019 | COLT | 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, Csar A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sbastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford |
| 2019 | COLT | Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization. | Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, Csar A. Uribe |