Distributed learning with compressed gradient differences K Mishchenko, E Gorbunov, M Takáč, P Richtárik
arXiv preprint arXiv:1901.09269, 2019
206 2019 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
145 2020 Stochastic optimization with heavy-tailed noise via accelerated gradient clipping E Gorbunov, M Danilova, A Gasnikov
Advances in Neural Information Processing Systems 33, 15042-15053, 2020
102 2020 Local sgd: Unified theory and new efficient methods E Gorbunov, F Hanzely, P Richtárik
International Conference on Artificial Intelligence and Statistics, 3556-3564, 2021
98 2021 MARINA: Faster non-convex distributed learning with compression E Gorbunov, KP Burlachenko, Z Li, P Richtárik
International Conference on Machine Learning, 3788-3798, 2021
94 2021 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
79 2019 Linearly converging error compensated SGD E Gorbunov, D Kovalev, D Makarenko, P Richtárik
Advances in Neural Information Processing Systems 33, 20889-20900, 2020
76 2020 Optimal decentralized distributed algorithms for stochastic convex optimization E Gorbunov, D Dvinskikh, A Gasnikov
arXiv preprint arXiv:1911.07363, 2019
70 2019 Optimal tensor methods in smooth convex and uniformly convexoptimization A Gasnikov, P Dvurechensky, E Gorbunov, E Vorontsova, ...
Conference on Learning Theory, 1374-1391, 2019
70 * 2019 Extragradient method: O (1/k) last-iterate convergence for monotone variational inequalities and connections with cocoercivity E Gorbunov, N Loizou, G Gidel
International Conference on Artificial Intelligence and Statistics, 366-402, 2022
68 2022 An accelerated method for derivative-free smooth stochastic convex optimization E Gorbunov, P Dvurechensky, A Gasnikov
SIAM Journal on Optimization 32 (2), 1210-1238, 2022
63 * 2022 Recent theoretical advances in non-convex optimization M Danilova, P Dvurechensky, A Gasnikov, E Gorbunov, S Guminov, ...
High-Dimensional Optimization and Probability: With a View Towards Data …, 2022
60 2022 Stochastic three points method for unconstrained smooth minimization EH Bergou, E Gorbunov, P Richtárik
SIAM Journal on Optimization 30 (4), 2726-2749, 2020
50 2020 EF21 with bells & whistles: Practical algorithmic extensions of modern error feedback I Fatkhullin, I Sokolov, E Gorbunov, Z Li, P Richtárik
arXiv preprint arXiv:2110.03294, 2021
44 2021 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
44 2021 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
40 * 2019 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
38 * 2020 Recent theoretical advances in decentralized distributed convex optimization E Gorbunov, A Rogozin, A Beznosikov, D Dvinskikh, A Gasnikov
High-Dimensional Optimization and Probability: With a View Towards Data …, 2022
36 2022 Near-optimal high probability complexity bounds for non-smooth stochastic optimization with heavy-tailed noise E Gorbunov, M Danilova, I Shibaev, P Dvurechensky, A Gasnikov
arXiv preprint arXiv:2106.05958, 2021
35 * 2021 Stochastic gradient descent-ascent: Unified theory and new efficient methods A Beznosikov, E Gorbunov, H Berard, N Loizou
International Conference on Artificial Intelligence and Statistics, 172-235, 2023
34 2023