| Federated learning of a mixture of global and local models F Hanzely, P Richtárik arXiv preprint arXiv:2002.05516, 2020 | 469 | 2020 |
| A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 416 | 2021 |
| Lower bounds and optimal algorithms for personalized federated learning F Hanzely, S Hanzely, S Horváth, P Richtárik NeurIPS 2020, 2020 | 207 | 2020 |
| 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 | 180 | 2020 |
| Local SGD: Unified theory and new efficient methods E Gorbunov, F Hanzely, P Richtárik International Conference on Artificial Intelligence and Statistics, 3556-3564, 2021 | 129 | 2021 |
| Accelerated Bregman proximal gradient methods for relatively smooth convex optimization F Hanzely, P Richtarik, L Xiao Computational Optimization and Applications, 2018 | 94 | 2018 |
| SEGA: Variance reduction via gradient sketching F Hanzely, K Mishchenko, P Richtárik NeruIPS 2018, 2018 | 93 | 2018 |
| Fastest rates for stochastic mirror descent methods F Hanzely, P Richtárik Computational Optimization and Applications 79, 717-766, 2021 | 61 | 2021 |
| Stochastic Subspace Cubic Newton Method F Hanzely, N Doikov, P Richtárik, Y Nesterov ICML 2020, 2020 | 59 | 2020 |
| Accelerated stochastic matrix inversion: general theory and speeding up BFGS rules for faster second-order optimization RM Gower, F Hanzely, P Richtárik, S Stich NeurIPS 2018, 2018 | 54 | 2018 |
| Personalized federated learning: A unified framework and universal optimization techniques F Hanzely, B Zhao, M Kolar arXiv preprint arXiv:2102.09743, 2021 | 50 | 2021 |
| Accelerated coordinate descent with arbitrary sampling and best rates for minibatches F Hanzely, P Richtárik AISTATS 2019, 2019 | 48 | 2019 |
| Testing for causality in reconstructed state spaces by an optimized mixed prediction method A Krakovská, F Hanzely Physical Review E 94 (5), 052203, 2016 | 37 | 2016 |
| 99% of worker-master communication in distributed optimization is not needed K Mishchenko, F Hanzely, P Richtárik Conference on Uncertainty in Artificial Intelligence, 979-988, 2020 | 35* | 2020 |
| Smoothness matrices beat smoothness constants: Better communication compression techniques for distributed optimization M Safaryan, F Hanzely, P Richtárik Advances in Neural Information Processing Systems 34, 25688-25702, 2021 | 31 | 2021 |
| One method to rule them all: Variance reduction for data, parameters and many new methods F Hanzely, P Richtárik arXiv preprint arXiv:1905.11266, 2019 | 29 | 2019 |
| Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems F Hanzely, D Kovalev, P Richtarik ICML 2020, 2020 | 23 | 2020 |
| Privacy preserving randomized gossip algorithms F Hanzely, J Konečný, N Loizou, P Richtárik, D Grishchenko arXiv preprint arXiv:1706.07636, 2017 | 22 | 2017 |
| A nonconvex projection method for robust PCA A Dutta, F Hanzely, P Richtárik Proceedings of the AAAI conference on artificial intelligence 33 (01), 1468-1476, 2019 | 20 | 2019 |
| A privacy preserving randomized gossip algorithm via controlled noise insertion F Hanzely, J Konečný, N Loizou, P Richtárik, D Grishchenko NeurIPS PPML workshop 2018, 2018 | 8 | 2018 |
Peter RichtarikProfessor, KAUST確認したメール アドレス: kaust.edu.sa