| SGD: General analysis and improved rates RM Gower, N Loizou, X Qian, A Sailanbayev, E Shulgin, P Richtárik International Conference on Machine Learning, 5200-5209, 2019 | 474 | 2019 |
| Revisiting stochastic extragradient K Mishchenko, D Kovalev, E Shulgin, P Richtárik, Y Malitsky International Conference on Artificial Intelligence and Statistics, 4573-4582, 2020 | 90 | 2020 |
| Uncertainty principle for communication compression in distributed and federated learning and the search for an optimal compressor M Safaryan, E Shulgin, P Richtárik Information and Inference: A Journal of the IMA 11 (2), 557-580, 2022 | 58 | 2022 |
| Adaptive catalyst for smooth convex optimization A Ivanova, D Pasechnyuk, D Grishchenko, E Shulgin, A Gasnikov, ... International Conference on Optimization and Applications, 20-37, 2021 | 37 | 2021 |
| 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 | 35 | 2021 |
| Towards accelerated rates for distributed optimization over time-varying networks A Rogozin, V Lukoshkin, A Gasnikov, D Kovalev, E Shulgin Optimization and Applications: 12th International Conference, OPTIMA 2021 …, 2021 | 30 | 2021 |
| Shifted compression framework: Generalizations and improvements E Shulgin, P Richtárik Uncertainty in Artificial Intelligence, 1813-1823, 2022 | 7 | 2022 |
| Certified Robustness in Federated Learning M Alfarra, JC Pérez, E Shulgin, P Richtárik, B Ghanem NeurIPS 2022 Workshop Federated Learning, 2022 | 7 | 2022 |
| Towards a Better Theoretical Understanding of Independent Subnetwork Training E Shulgin, P Richtárik International Conference on Machine Learning (ICML), 2023 | 6 | 2023 |
| SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning A Karagulyan, E Shulgin, A Sadiev, P Richtárik arXiv preprint arXiv:2405.20127, 2024 | 3 | 2024 |
| Lecture notes on stochastic processes A Gasnikov, E Gorbunov, S Guz, E Chernousova, M Shirobokov, ... arXiv preprint arXiv:1907.01060, 2019 | 1 | 2019 |
| MAST: Model-Agnostic Sparsified Training Y Demidovich, G Malinovsky, E Shulgin, P Richtárik arXiv preprint arXiv:2311.16086, 2023 | | 2023 |
| MotasemAlfarra/federated-learning-with-pytorch M Alfarra, JC Pérez, E Shulgin, P Richtarik, B Ghanem Github, 2022 | | 2022 |
| SGD: General Analysis and Improved Rates R Mansel Gower, N Loizou, X Qian, A Sailanbayev, E Shulgin, P Richtarik arXiv e-prints, arXiv: 1901.09401, 2019 | | 2019 |
Peter RichtarikProfessor, KAUSTEmail verificata su kaust.edu.sa
