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Eduard Gorbunov
Eduard Gorbunov
Other namesЭдуард Александрович Горбунов, Eduard A. Gorbunov
Postdoctoral Fellow, Mohamed bin Zayed University of Artificial Intelligence
Verified email at mbzuai.ac.ae - Homepage
Title
Cited by
Cited by
Year
Distributed learning with compressed gradient differences
K Mishchenko, E Gorbunov, M Takáč, P Richtárik
arXiv preprint arXiv:1901.09269, 2019
2092019
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
1482020
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
1032020
Local sgd: Unified theory and new efficient methods
E Gorbunov, F Hanzely, P Richtárik
International Conference on Artificial Intelligence and Statistics, 3556-3564, 2021
982021
MARINA: Faster non-convex distributed learning with compression
E Gorbunov, KP Burlachenko, Z Li, P Richtárik
International Conference on Machine Learning, 3788-3798, 2021
972021
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
802019
Linearly converging error compensated SGD
E Gorbunov, D Kovalev, D Makarenko, P Richtárik
Advances in Neural Information Processing Systems 33, 20889-20900, 2020
772020
Optimal tensor methods in smooth convex and uniformly convexoptimization
A Gasnikov, P Dvurechensky, E Gorbunov, E Vorontsova, ...
Conference on Learning Theory, 1374-1391, 2019
72*2019
Optimal decentralized distributed algorithms for stochastic convex optimization
E Gorbunov, D Dvinskikh, A Gasnikov
arXiv preprint arXiv:1911.07363, 2019
702019
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
682022
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
64*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
622022
Stochastic three points method for unconstrained smooth minimization
EH Bergou, E Gorbunov, P Richtárik
SIAM Journal on Optimization 30 (4), 2726-2749, 2020
502020
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
452021
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
442021
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
41*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
372022
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
36*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
342023
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