| On Biased Compression for Distributed Learning A Beznosikov, S Horváth, P Richtárik, M Safaryan Journal of Machine Learning Research (JMLR), 2023, 2020 | 208 | 2020 |
| FedNL: Making Newton-type methods applicable to federated learning M Safaryan, R Islamov, X Qian, P Richtárik International Conference on Machine Learning (ICML), 2022, 2021 | 91 | 2021 |
| Optimal Gradient Compression for Distributed and Federated Learning A Albasyoni, M Safaryan, L Condat, P Richtárik arXiv preprint arXiv:2010.03246, 2020 | 68 | 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, 2021, 2020 | 65 | 2020 |
| Stochastic Sign Descent Methods: New Algorithms and Better Theory M Safaryan, P Richtárik International Conference on Machine Learning (ICML), 2021, 2019 | 57* | 2019 |
| Smoothness matrices beat smoothness constants: Better communication compression techniques for distributed optimization M Safaryan, F Hanzely, P Richtárik Advances in Neural Information Processing Systems (NeurIPS) 34, 25688-25702, 2021 | 31 | 2021 |
| Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning X Qian, R Islamov, M Safaryan, P Richtárik International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 26 | 2021 |
| Construction of free g-dimonoids Y Movsisyan, S Davidov, M Safaryan Algebra and discrete mathematics, 2014 | 18 | 2014 |
| AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms R Islamov, M Safaryan, D Alistarh International Conference on Artificial Intelligence and Statistics (AISTATS …, 2023 | 15 | 2023 |
| Distributed Newton-type methods with communication compression and bernoulli aggregation R Islamov, X Qian, S Hanzely, M Safaryan, P Richtárik Transactions on Machine Learning Research (TMLR), 2023, 2022 | 14 | 2022 |
| Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques B Wang, M Safaryan, P Richtárik Advances in Neural Information Processing Systems (NeurIPS) 2022, 2021 | 14* | 2021 |
| Gradskip: Communication-accelerated local gradient methods with better computational complexity A Maranjyan, M Safaryan, P Richtárik arXiv preprint arXiv:2210.16402, 2022 | 12 | 2022 |
| On generalizations of Fatou’s theorem for the integrals with general kernels GA Karagulyan, MH Safaryan The Journal of Geometric Analysis 25, 1459-1475, 2015 | 10 | 2015 |
| MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence IV Modoranu, M Safaryan, G Malinovsky, E Kurtic, T Robert, P Richtarik, ... Advances in Neural Information Processing Systems (NeurIPS) 2024, 2024 | 9* | 2024 |
| On Generalizations of Fatou’s Theorem in for Convolution Integrals with General Kernels MH Safaryan The Journal of Geometric Analysis 31 (4), 3280-3299, 2021 | 8 | 2021 |
| On a theorem of Littlewood GA Karagulyan, MH Safaryan Hokkaido Mathematical Journal 46 (1), 87-106, 2017 | 6 | 2017 |
| On an equivalence for differentiation bases of dyadic rectangles GA Karagulyan, DA Karagulyan, MH Safaryan Colloq. Math 146 (2), 295-307, 2017 | 6 | 2017 |
| On an equivalency of rare differentiation bases of rectangles MH Safaryan Journal of Contemporary Mathematical Analysis (Armenian Academy of Sciences …, 2018 | 5 | 2018 |
| Knowledge Distillation Performs Partial Variance Reduction M Safaryan, A Peste, D Alistarh Advances in Neural Information Processing Systems (NeurIPS) 2023, 2023 | 4 | 2023 |
| LDAdam: Adaptive Optimization from Low-dimensional Gradient Statistics T Robert, M Safaryan, IV Modoranu, D Alistarh International Conference on Learning Representations (ICLR), 2025, 2024 | 3 | 2024 |
Peter RichtarikProfessor, KAUST確認したメール アドレス: kaust.edu.sa