| Federated learning: Strategies for improving communication efficiency J Konečný, HB McMahan, FX Yu, P Richtárik, AT Suresh, D Bacon arXiv preprint arXiv:1610.05492 [NIPS Private Multi-Party Machine Learning …, 2016 | 1725 | 2016 |
| Federated optimization: Distributed machine learning for on-device intelligence J Konečný, HB McMahan, D Ramage, P Richtárik arXiv preprint arXiv:1610.02527, 2016 | 727 | 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 | 710 | 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 | 600 | 2010 |
| Parallel coordinate descent methods for big data optimization P Richtárik, M Takáč Mathematical Programming 156 (1), 433-484, 2016 | 477 | 2016 |
| Accelerated, parallel and proximal coordinate descent O Fercoq, P Richtárik SIAM Journal on Optimization 25 (4), 1997-2023, 2015 | 356 | 2015 |
| 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 | 251 | 2016 |
| Semi-stochastic gradient descent methods J Konečný, P Richtárik Frontiers in Applied Mathematics and Statistics 3:9, 2017 | 234* | 2017 |
| Distributed coordinate descent method for learning with big data P Richtárik, M Takáč Journal of Machine Learning Research 17 (75), 1-25, 2016 | 212 | 2016 |
| Randomized iterative methods for linear systems RM Gower, P Richtárik SIAM Journal on Matrix Analysis and Applications 36 (4), 1660-1690, 2015 | 199 | 2015 |
| Mini-batch primal and dual methods for SVMs M Takáč, A Bijral, P Richtárik, N Srebro Proceedings of the 30th Int. Conf. on Machine Learning, PMLR 28 (3), 1022-1030, 2013 | 197* | 2013 |
| Adding vs. averaging in distributed primal-dual optimization C Ma, V Smith, M Jaggi, MI Jordan, P Richtárik, M Takáč Proceedings of the 32nd Int. Conf. on Machine Learning, PMLR 37, 1973-1982, 2015 | 173 | 2015 |
| 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 | 158 | 2016 |
| SGD: General Analysis and Improved Rates RM Gower, N Loizou, X Qian, A Sailanbayev, E Shulgin, P Richtarik ICML 2019, 2019 | 139 | 2019 |
| Stochastic block BFGS: squeezing more curvature out of data RM Gower, D Goldfarb, P Richtárik Proceedings of The 33rd Int. Conf. on Machine Learning, PMLR 48, 1869-1878, 2016 | 137 | 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 | 121 | 2018 |
| Coordinate descent with arbitrary sampling I: algorithms and complexity Z Qu, P Richtárik Optimization Methods and Software 31 (5), 829-857, 2016 | 120 | 2016 |
| 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 | 117 | 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 | 112 | 2020 |
| On optimal probabilities in stochastic coordinate descent methods P Richtárik, M Takáč Optimization Letters 10 (6), 1233-1243, 2016 | 110 | 2016 |
Martin TakáčMohamed Bin Zayed University of Artificial IntelligenceVerified email at lehigh.edu
