Eduard A. Gorbunov

Publication Activity (10 Years)

Years Active: 2018-2022
Publications (10 Years): 26

Top Topics

Optimization Methods

Variance Reduction

Convex Optimization

Top Venues

Autom. Remote. Control.

Publications

Peter Richtárik, Igor Sokolov, Ilyas Fatkhullin, Elnur Gasanov, Zhize Li, Eduard A. Gorbunov
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. CoRR (2022)
Eduard A. Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik
MARINA: Faster Non-Convex Distributed Learning with Compression. CoRR (2021)
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
Local SGD: Unified Theory and New Efficient Methods. AISTATS (2021)
Eduard A. Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik
MARINA: Faster Non-Convex Distributed Learning with Compression. ICML (2021)
Eduard A. Gorbunov, Marina Danilova, Innokentiy Shibaev, Pavel E. Dvurechensky, Alexander V. Gasnikov
Near-Optimal High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise. CoRR (2021)
Pavel E. Dvurechensky, Eduard A. Gorbunov, Alexander V. Gasnikov
An accelerated directional derivative method for smooth stochastic convex optimization. Eur. J. Oper. Res. 290 (2) (2021)
Ilyas Fatkhullin, Igor Sokolov, Eduard A. Gorbunov, Zhize Li, Peter Richtárik
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback. CoRR (2021)
Eduard A. Gorbunov, Marina Danilova, Alexander V. Gasnikov
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping. NeurIPS (2020)
Marina Danilova, Pavel E. Dvurechensky, Alexander V. Gasnikov, Eduard A. Gorbunov, Sergey Guminov, Dmitry Kamzolov, Innokentiy Shibaev
Recent Theoretical Advances in Non-Convex Optimization. CoRR (2020)
Eduard A. Gorbunov, Adel Bibi, Ozan Sener, El Houcine Bergou, Peter Richtárik
A Stochastic Derivative Free Optimization Method with Momentum. ICLR (2020)
Eduard A. Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtárik
Linearly Converging Error Compensated SGD. CoRR (2020)
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. AISTATS (2020)
El Houcine Bergou, Eduard A. Gorbunov, Peter Richtárik
Stochastic Three Points Method for Unconstrained Smooth Minimization. SIAM J. Optim. 30 (4) (2020)
Eduard A. Gorbunov, Marina Danilova, Alexander V. Gasnikov
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping. CoRR (2020)
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
Local SGD: Unified Theory and New Efficient Methods. CoRR (2020)
Eduard A. Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtárik
Linearly Converging Error Compensated SGD. NeurIPS (2020)
Konstantin Mishchenko, Eduard A. Gorbunov, Martin Takác, Peter Richtárik
Distributed Learning with Compressed Gradient Differences. CoRR (2019)
Evgeniya A. Vorontsova, Alexander V. Gasnikov, Eduard A. Gorbunov
Accelerated Directional Search with Non-Euclidean Prox-Structure. Autom. Remote. Control. 80 (4) (2019)
Eduard A. Gorbunov, Filip Hanzely, Peter Richtárik
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. CoRR (2019)
Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard A. Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford
Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. COLT (2019)
Evgeniya A. Vorontsova, Alexander V. Gasnikov, Eduard A. Gorbunov, Pavel E. Dvurechenskii
Accelerated Gradient-Free Optimization Methods with a Non-Euclidean Proximal Operator. Autom. Remote. Control. 80 (8) (2019)
Darina Dvinskikh, Eduard A. Gorbunov, Alexander V. Gasnikov, Pavel E. Dvurechensky, César A. Uribe
On Primal and Dual Approaches for Distributed Stochastic Convex Optimization over Networks. CDC (2019)
Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard A. Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, César A. Uribe
Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization. COLT (2019)
Pavel E. Dvurechensky, Alexander V. Gasnikov, Eduard A. Gorbunov
An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization. CoRR (2018)
Dmitry Kovalev, Peter Richtárik, Eduard A. Gorbunov, Elnur Gasanov
Stochastic Spectral and Conjugate Descent Methods. NeurIPS (2018)
Pavel E. Dvurechensky, Alexander V. Gasnikov, Eduard A. Gorbunov
An Accelerated Directional Derivative Method for Smooth Stochastic Convex Optimization. CoRR (2018)