| Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function P Richtárik, M Takáč Mathematical Programming 144 (1-2), 1-38, 2014 | 581 | 2014 |
| Parallel coordinate descent methods for big data optimization P Richtárik, M Takáč Mathematical Programming, Series A, 1-52, 2015 | 386 | 2015 |
| Communication-efficient distributed dual coordinate ascent M Jaggi, V Smith, M Takác, J Terhorst, S Krishnan, T Hofmann, MI Jordan Advances in neural information processing systems, 3068-3076, 2014 | 238 | 2014 |
| 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, 2015 | 175* | 2015 |
| Mini-batch primal and dual methods for SVMs M Takáč, A Bijral, P Richtárik, N Srebro In 30th International Conference on Machine Learning, ICML 2013, 2013 | 168* | 2013 |
| Distributed coordinate descent method for learning with big data P Richtárik, M Takác Journal of Machine Learning Research 17, 1-25, 2016 | 162 | 2016 |
| SARAH: A novel method for machine learning problems using stochastic recursive gradient L Nguyen, J Liu, K Scheinberg, M Takáč In 34th International Conference on Machine Learning, ICML 2017, 2017 | 127 | 2017 |
| Adding vs. averaging in distributed primal-dual optimization C Ma, V Smith, M Jaggi, MI Jordan, P Richtárik, M Takáč arXiv preprint arXiv:1502.03508, 2015 | 122 | 2015 |
| Adding vs. averaging in distributed primal-dual optimization C Ma, V Smith, M Jaggi, MI Jordan, P Richtárik, M Takáč In 32nd International Conference on Machine Learning, ICML 2015, 2015 | 122 | 2015 |
| CoCoA: A general framework for communication-efficient distributed optimization V Smith, S Forte, C Ma, M Takáč, MI Jordan, M Jaggi The Journal of Machine Learning Research 18 (1), 8590-8638, 2017 | 88 | 2017 |
| On optimal probabilities in stochastic coordinate descent methods P Richtárik, M Takáč Optimization Letters, 2015, 1-11, 2015 | 82 | 2015 |
| SDNA: stochastic dual newton ascent for empirical risk minimization Z Qu, P Richtárik, M Takáč, O Fercoq In 33rd International Conference on Machine Learning, ICML 2016, 2016 | 64 | 2016 |
| Fast distributed coordinate descent for non-strongly convex losses O Fercoq, Z Qu, P Richtárik, M Takáč IEEE Workshop on Machine Learning for Signal Processing, 2014, 2014 | 54 | 2014 |
| 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 | 52 | 2017 |
| Efficient serial and parallel coordinate descent methods for huge-scale truss topology design P Richtárik, M Takáč Operations Research Proceedings 2011, 27-32, 2012 | 51 | 2012 |
| SGD and Hogwild! Convergence Without the Bounded Gradients Assumption LM Nguyen, PH Nguyen, M van Dijk, P Richtárik, K Scheinberg, M Takáč In 34th International Conference on Machine Learning, ICML 2018, 2018 | 48 | 2018 |
| Distributed block coordinate descent for minimizing partially separable functions J Marecek, P Richtárik, M Takac Numerical Analysis and Optimization 2014, Springer Proceedings in …, 2014 | 48 | 2014 |
| A Multi-Batch L-BFGS Method for Machine Learning AS Berahas, J Nocedal, M Takáč The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), 2016 | 47 | 2016 |
| Reinforcement learning for solving the vehicle routing problem M Nazari, A Oroojlooy, L Snyder, M Takác Advances in Neural Information Processing Systems, 9839-9849, 2018 | 46 | 2018 |
| Stochastic recursive gradient algorithm for nonconvex optimization LM Nguyen, J Liu, K Scheinberg, M Takáč arXiv preprint arXiv:1705.07261, 2017 | 44 | 2017 |
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
