Federated learning: Strategies for improving communication efficiency J Konečný, HB McMahan, FX Yu, P Richtárik, AT Suresh, D Bacon arXiv preprint arXiv:1610.05492, 2016 | 5209 | 2016 |
Federated optimization: Distributed machine learning for on-device intelligence J Konečný, HB McMahan, D Ramage, P Richtárik arXiv preprint arXiv:1610.02527, 2016 | 2134 | 2016 |
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function P Richtarik, M Takáč Mathematical Programming 144 (2), 1-38, 2014 | 854 | 2014 |
Generalized power method for sparse principal component analysis M Journee, Y Nesterov, P Richtárik, R Sepulchre Journal of Machine Learning Research 11, 517-553, 2010 | 745 | 2010 |
Parallel coordinate descent methods for big data optimization P Richtárik, M Takáč Mathematical Programming 156 (1), 433-484, 2016 | 538 | 2016 |
Tighter theory for local SGD on identical and heterogeneous data A Khaled, K Mishchenko, P Richtárik The 23rd International Conference on Artificial Intelligence and Statistics, 2020 | 429 | 2020 |
SGD: General Analysis and Improved Rates RM Gower, N Loizou, X Qian, A Sailanbayev, E Shulgin, P Richtarik ICML 2019, 2019 | 427 | 2019 |
Accelerated, parallel and proximal coordinate descent O Fercoq, P Richtárik SIAM Journal on Optimization 25 (4), 1997-2023, 2015 | 413 | 2015 |
Scaling distributed machine learning with {In-Network} aggregation A Sapio, M Canini, CY Ho, J Nelson, P Kalnis, C Kim, A Krishnamurthy, ... 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2021 | 407 | 2021 |
Federated learning of a mixture of global and local models F Hanzely, P Richtárik arXiv preprint arXiv:2002.05516, 2020 | 388 | 2020 |
Mini-batch semi-stochastic gradient descent in the proximal setting J Konečný, J Liu, P Richtárik, M Takáč IEEE Journal of Selected Topics in Signal Processing 10 (2), 242-255, 2016 | 330 | 2016 |
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 | 327 | 2021 |
Randomized iterative methods for linear systems RM Gower, P Richtárik SIAM Journal on Matrix Analysis and Applications 36 (4), 1660-1690, 2015 | 323 | 2015 |
Semi-stochastic gradient descent methods J Konečný, P Richtárik Frontiers in Applied Mathematics and Statistics 3:9, 2017 | 273 | 2017 |
Distributed coordinate descent method for learning with big data P Richtárik, M Takáč Journal of Machine Learning Research 17 (75), 1-25, 2016 | 266 | 2016 |
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption LM Nguyen, PH Nguyen, M van Dijk, P Richtárik, K Scheinberg, M Takáč Proceedings of the 35th Int. Conf. on Machine Learning, PMLR 80, 3750-3758, 2018 | 231 | 2018 |
Distributed learning with compressed gradient differences K Mishchenko, E Gorbunov, M Takáč, P Richtárik arXiv preprint arXiv:1901.09269, 2019 | 221 | 2019 |
Distributed optimization with arbitrary local solvers C Ma, J Konečný, M Jaggi, V Smith, MI Jordan, P Richtárik, M Takáč Optimization Methods and Software 32 (4), 813-848, 2017 | 216 | 2017 |
Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods N Loizou, P Richtárik Computational Optimization and Applications 77 (3), 653-710, 2020 | 215 | 2020 |
Even faster accelerated coordinate descent using non-uniform sampling Z Allen-Zhu, Z Qu, P Richtarik, Y Yuan Proceedings of The 33rd Int. Conf. on Machine Learning, PMLR 48, 1110-1119, 2016 | 210 | 2016 |