Eduard Gorbunov
Title
Cited by
Cited by
Year
Distributed learning with compressed gradient differences
K Mishchenko, E Gorbunov, M Takáè, P Richtárik
arXiv preprint arXiv:1901.09269, 2019
672019
An accelerated method for derivative-free smooth stochastic convex optimization
E Gorbunov, P Dvurechensky, A Gasnikov
arXiv preprint arXiv:1802.09022, 2018
44*2018
A unified theory of sgd: Variance reduction, sampling, quantization and coordinate descent
E Gorbunov, F Hanzely, P Richtárik
International Conference on Artificial Intelligence and Statistics, 680-690, 2020
432020
Optimal decentralized distributed algorithms for stochastic convex optimization
E Gorbunov, D Dvinskikh, A Gasnikov
arXiv preprint arXiv:1911.07363, 2019
372019
The global rate of convergence for optimal tensor methods in smooth convex optimization
A Gasnikov, P Dvurechensky, E Gorbunov, E Vorontsova, ...
arXiv preprint arXiv:1809.00382, 2018
35*2018
Near Optimal Methods for Minimizing Convex Functions with Lipschitz -th Derivatives
A Gasnikov, P Dvurechensky, E Gorbunov, E Vorontsova, ...
Conference on Learning Theory, 1392-1393, 2019
282019
On primal and dual approaches for distributed stochastic convex optimization over networks
D Dvinskikh, E Gorbunov, A Gasnikov, P Dvurechensky, CA Uribe
2019 IEEE 58th Conference on Decision and Control (CDC), 7435-7440, 2019
24*2019
Stochastic three points method for unconstrained smooth minimization
EH Bergou, E Gorbunov, P Richtárik
SIAM Journal on Optimization 30 (4), 2726-2749, 2020
22*2020
Derivative-free method for composite optimization with applications to decentralized distributed optimization
A Beznosikov, E Gorbunov, A Gasnikov
IFAC-PapersOnLine 53 (2), 4038-4043, 2020
19*2020
An accelerated directional derivative method for smooth stochastic convex optimization
P Dvurechensky, E Gorbunov, A Gasnikov
European Journal of Operational Research 290 (2), 601-621, 2021
182021
Stochastic optimization with heavy-tailed noise via accelerated gradient clipping
E Gorbunov, M Danilova, A Gasnikov
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) [arXiv …, 2020
162020
Accelerated directional search with non-Euclidean prox-structure
EA Vorontsova, AV Gasnikov, EA Gorbunov
Automation and Remote Control 80 (4), 693-707, 2019
15*2019
Local sgd: Unified theory and new efficient methods
E Gorbunov, F Hanzely, P Richtárik
International Conference on Artificial Intelligence and Statistics, 3556-3564, 2021
122021
Linearly Converging Error Compensated SGD
E Gorbunov, D Kovalev, D Makarenko, P Richtárik
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) [arXiv …, 2020
112020
A stochastic derivative free optimization method with momentum
E Gorbunov, A Bibi, O Sener, EH Bergou, P Richtárik
8th International Conference on Learning Representations (ICLR 2020) [arXiv …, 2019
102019
Stochastic spectral and conjugate descent methods
D Kovalev, E Gorbunov, E Gasanov, P Richtárik
arXiv preprint arXiv:1802.03703, 2018
72018
Recent theoretical advances in decentralized distributed convex optimization
E Gorbunov, A Rogozin, A Beznosikov, D Dvinskikh, A Gasnikov
arXiv preprint arXiv:2011.13259, 2020
62020
MARINA: Faster Non-Convex Distributed Learning with Compression
E Gorbunov, K Burlachenko, Z Li, P Richtárik
International Conference on Machine Learning (ICML 2021), 2021
42021
Recent theoretical advances in non-convex optimization
M Danilova, P Dvurechensky, A Gasnikov, E Gorbunov, S Guminov, ...
arXiv preprint arXiv:2012.06188, 2020
42020
Accelerated gradient-free optimization methods with a non-Euclidean proximal operator
EA Vorontsova, AV Gasnikov, EA Gorbunov, PE Dvurechenskii
Automation and Remote Control 80 (8), 1487-1501, 2019
42019
The system can't perform the operation now. Try again later.
Articles 1–20