Eduard Gorbunov

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

2023-12-31

International Conference on Machine Learning (publié)

doi.org

proceedings.mlr.press

Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity

Eduard Gorbunov

Adrien Taylor

Samuel Horváth

Gauthier Gidel

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

proceedings.mlr.press

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

During the recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimiza… (voir plus)tion methods has been growing. One of the main reasons for this is that high-probability complexity bounds are more accurate and less studied than in-expectation ones. However, SOTA high-probability non-asymptotic convergence results are derived under strong assumptions such as boundedness of the gradient noise variance or of the objective’s gradient itself. In this paper, we propose several algorithms with high-probability convergence results under less restrictive assumptions. In particular, we derive new high-probability convergence results under the assumption that the gradient/operator noise has bounded central

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

doi.org

proceedings.mlr.press

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

Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in collaborative and federated learning. However,… (voir plus) many fruitful directions, such as the usage of variance reduction for achieving robustness and communication compression for reducing communication costs, remain weakly explored in the field. This work addresses this gap and proposes Byz-VR-MARINA - a new Byzantine-tolerant method with variance reduction and compression. A key message of our paper is that variance reduction is key to fighting Byzantine workers more effectively. At the same time, communication compression is a bonus that makes the process more communication efficient. We derive theoretical convergence guarantees for Byz-VR-MARINA outperforming previous state-of-the-art for general non-convex and Polyak-Lojasiewicz loss functions. Unlike the concurrent Byzantine-robust methods with variance reduction and/or compression, our complexity results are tight and do not rely on restrictive assumptions such as boundedness of the gradients or limited compression. Moreover, we provide the first analysis of a Byzantine-tolerant method supporting non-uniform sampling of stochastic gradients. Numerical experiments corroborate our theoretical findings.

2022-12-31

International Conference on Learning Representations (publié)

doi.org

openreview.net

Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise

Eduard Gorbunov

Marina Danilova

David Dobre

Pavel Dvurechensky

Alexander Gasnikov

Gauthier Gidel

2021-12-31