| 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 | 1061 | 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 | 657 | 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 | 550 | 2010 |
| Parallel coordinate descent methods for big data optimization P Richtárik, M Takáč Mathematical Programming 156 (1), 433-484, 2016 | 443 | 2016 |
| Federated optimization: Distributed machine learning for on-device intelligence J Konečný, HB McMahan, D Ramage, P Richtárik arXiv preprint arXiv:1610.02527, 2016 | 429 | 2016 |
| Accelerated, parallel and proximal coordinate descent O Fercoq, P Richtárik SIAM Journal on Optimization 25 (4), 1997-2023, 2015 | 328 | 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 | 220* | 2016 |
| Semi-stochastic gradient descent methods J Konečný, P Richtárik Frontiers in Applied Mathematics and Statistics 3:9, 2017 | 218* | 2017 |
| Distributed coordinate descent method for learning with big data P Richtárik, M Takáč Journal of Machine Learning Research 17 (75), 1-25, 2016 | 198 | 2016 |
| 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 | 185* | 2013 |
| Randomized iterative methods for linear systems RM Gower, P Richtárik SIAM Journal on Matrix Analysis and Applications 36 (4), 1660-1690, 2015 | 166 | 2015 |
| 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 | 153 | 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 | 137 | 2016 |
| Quartz: Randomized dual coordinate ascent with arbitrary sampling Z Qu, P Richtárik, T Zhang Advances in Neural Information Processing Systems 28, 865-873, 2015 | 120 | 2015 |
| 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 | 114 | 2016 |
| Coordinate descent with arbitrary sampling I: algorithms and complexity Z Qu, P Richtárik Optimization Methods and Software 31 (5), 829-857, 2016 | 108 | 2016 |
| On optimal probabilities in stochastic coordinate descent methods P Richtárik, M Takáč Optimization Letters 10 (6), 1233-1243, 2016 | 97 | 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 | 90 | 2017 |
| Stochastic dual coordinate ascent with adaptive probabilities D Csiba, Z Qu, P Richtárik Proceedings of the 32nd Int. Conf. on Machine Learning, PMLR 37, 674-683, 2015 | 86 | 2015 |
| AIDE: Fast and communication efficient distributed optimization SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola arXiv preprint arXiv:1608.06879, 2016 | 85 | 2016 |
Martin TakáčLehigh UniversityVerified email at lehigh.edu
