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Dmitry Kovalev
Dmitry Kovalev
Yandex
Подтвержден адрес электронной почты в домене kaust.edu.sa - Главная страница
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Процитировано
Год
Stochastic distributed learning with gradient quantization and double-variance reduction
S Horváth, D Kovalev, K Mishchenko, P Richtárik, S Stich
Optimization Methods and Software 38 (1), 91-106, 2023
2032023
Don’t jump through hoops and remove those loops: SVRG and Katyusha are better without the outer loop
D Kovalev, S Horváth, P Richtárik
Algorithmic Learning Theory, 451-467, 2020
1892020
Acceleration for compressed gradient descent in distributed and federated optimization
Z Li, D Kovalev, X Qian, P Richtarik
International Conference on Machine Learning, 5895-5904, 2020
1692020
From local SGD to local fixed-point methods for federated learning
G Malinovskiy, D Kovalev, E Gasanov, L Condat, P Richtarik
International Conference on Machine Learning, 6692-6701, 2020
1462020
RSN: randomized subspace Newton
R Gower, D Kovalev, F Lieder, P Richtárik
Advances in Neural Information Processing Systems 32, 2019
1092019
Revisiting stochastic extragradient
K Mishchenko, D Kovalev, E Shulgin, P Richtárik, Y Malitsky
International Conference on Artificial Intelligence and Statistics, 4573-4582, 2020
992020
Optimal and practical algorithms for smooth and strongly convex decentralized optimization
D Kovalev, A Salim, P Richtárik
Advances in Neural Information Processing Systems 33, 18342-18352, 2020
942020
Linearly converging error compensated SGD
E Gorbunov, D Kovalev, D Makarenko, P Richtárik
Advances in Neural Information Processing Systems 33, 20889-20900, 2020
922020
A linearly convergent algorithm for decentralized optimization: Sending less bits for free!
D Kovalev, A Koloskova, M Jaggi, P Richtarik, S Stich
International Conference on Artificial Intelligence and Statistics, 4087-4095, 2021
892021
Accelerated methods for saddle-point problem
MS Alkousa, AV Gasnikov, DM Dvinskikh, DA Kovalev, FS Stonyakin
Computational Mathematics and Mathematical Physics 60, 1787-1809, 2020
69*2020
Stochastic Newton and cubic Newton methods with simple local linear-quadratic rates
D Kovalev, K Mishchenko, P Richtárik
arXiv preprint arXiv:1912.01597, 2019
512019
Accelerated primal-dual gradient method for smooth and convex-concave saddle-point problems with bilinear coupling
D Kovalev, A Gasnikov, P Richtárik
Advances in Neural Information Processing Systems 35, 21725-21737, 2022
482022
Lower bounds and optimal algorithms for smooth and strongly convex decentralized optimization over time-varying networks
D Kovalev, E Gasanov, A Gasnikov, P Richtarik
Advances in Neural Information Processing Systems 34, 22325-22335, 2021
462021
Decentralized distributed optimization for saddle point problems
A Rogozin, A Beznosikov, D Dvinskikh, D Kovalev, P Dvurechensky, ...
arXiv preprint arXiv:2102.07758, 2021
432021
The first optimal acceleration of high-order methods in smooth convex optimization
D Kovalev, A Gasnikov
Advances in Neural Information Processing Systems 35, 35339-35351, 2022
392022
Smooth monotone stochastic variational inequalities and saddle point problems: A survey
A Beznosikov, B Polyak, E Gorbunov, D Kovalev, A Gasnikov
European Mathematical Society Magazine, 15-28, 2023
372023
Optimal algorithms for decentralized stochastic variational inequalities
D Kovalev, A Beznosikov, A Sadiev, M Persiianov, P Richtárik, ...
Advances in Neural Information Processing Systems 35, 31073-31088, 2022
352022
ADOM: accelerated decentralized optimization method for time-varying networks
D Kovalev, E Shulgin, P Richtárik, AV Rogozin, A Gasnikov
International Conference on Machine Learning, 5784-5793, 2021
352021
Accelerated variance-reduced methods for saddle-point problems
E Borodich, V Tominin, Y Tominin, D Kovalev, A Gasnikov, ...
EURO Journal on Computational Optimization 10, 100048, 2022
34*2022
Optimal gradient sliding and its application to optimal distributed optimization under similarity
D Kovalev, A Beznosikov, E Borodich, A Gasnikov, G Scutari
Advances in Neural Information Processing Systems 35, 33494-33507, 2022
322022
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