| Proxskip: Yes! local gradient steps provably lead to communication acceleration! finally! K Mishchenko, G Malinovsky, S Stich, P Richtárik International Conference on Machine Learning, 15750-15769, 2022 | 167 | 2022 |
| 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 | 140 | 2020 |
| Variance reduced proxskip: Algorithm, theory and application to federated learning G Malinovsky, K Yi, P Richtárik Advances in Neural Information Processing Systems 35, 15176-15189, 2022 | 36 | 2022 |
| A guide through the zoo of biased SGD Y Demidovich, G Malinovsky, I Sokolov, P Richtárik Advances in Neural Information Processing Systems 36, 23158-23171, 2023 | 30 | 2023 |
| Server-side stepsizes and sampling without replacement provably help in federated optimization G Malinovsky, K Mishchenko, P Richtárik Proceedings of the 4th International Workshop on Distributed Machine …, 2023 | 27 | 2023 |
| Federated optimization algorithms with random reshuffling and gradient compression A Sadiev, G Malinovsky, E Gorbunov, I Sokolov, A Khaled, K Burlachenko, ... arXiv preprint arXiv:2206.07021, 2022 | 27 | 2022 |
| Can 5th generation local training methods support client sampling? yes! M Grudzień, G Malinovsky, P Richtárik International Conference on Artificial Intelligence and Statistics, 1055-1092, 2023 | 24 | 2023 |
| Distributed proximal splitting algorithms with rates and acceleration L Condat, G Malinovsky, P Richtárik Frontiers in Signal Processing 1, 776825, 2022 | 23 | 2022 |
| Random reshuffling with variance reduction: New analysis and better rates G Malinovsky, A Sailanbayev, P Richtárik Uncertainty in Artificial Intelligence, 1347-1357, 2023 | 19 | 2023 |
| Tamuna: Accelerated federated learning with local training and partial participation LP Condat, G Malinovsky, P Richtárik arXiv, 2023 | 17* | 2023 |
| Federated learning with regularized client participation G Malinovsky, S Horváth, K Burlachenko, P Richtárik arXiv preprint arXiv:2302.03662, 2023 | 16 | 2023 |
| Improving accelerated federated learning with compression and importance sampling M Grudzień, G Malinovsky, P Richtárik arXiv preprint arXiv:2306.03240, 2023 | 14 | 2023 |
| Byzantine robustness and partial participation can be achieved simultaneously: Just clip gradient differences G Malinovsky, E Gorbunov, S Horváth, P Richtárik Privacy Regulation and Protection in Machine Learning, 2023 | 8 | 2023 |
| MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence IV Modoranu, M Safaryan, G Malinovsky, E Kurtic, T Robert, P Richtarik, ... arXiv preprint arXiv:2405.15593, 2024 | 7* | 2024 |
| Federated random reshuffling with compression and variance reduction G Malinovsky, P Richtárik arXiv preprint arXiv:2205.03914, 2022 | 7 | 2022 |
| An optimal algorithm for strongly convex min-min optimization A Gasnikov, D Kovalev, G Malinovsky arXiv preprint arXiv:2212.14439, 2022 | 6 | 2022 |
| Averaged heavy-ball method Метод тяжелого шарика с усреднением MY Danilova, GS Malinovsky Izhevsk Institute of Computer Science, 2022 | 5* | 2022 |
| Minibatch stochastic three points method for unconstrained smooth minimization S Boucherouite, G Malinovsky, P Richtárik, EH Bergou Proceedings of the AAAI Conference on Artificial Intelligence 38 (18), 20344 …, 2024 | 2 | 2024 |
| Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization Y Demidovich, P Ostroukhov, G Malinovsky, S Horváth, M Takáč, ... arXiv preprint arXiv:2412.02781, 2024 | 1 | 2024 |
| Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation G Malinovsky, U Michieli, HAAK Hammoud, T Ceritli, H Elesedy, M Ozay, ... arXiv preprint arXiv:2410.08305, 2024 | 1 | 2024 |
Peter RichtarikProfessor, KAUST確認したメール アドレス: kaust.edu.sa
