| Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function P Richtárik, M Takáč Mathematical Programming 144 (1), 1-38, 2014 | 675 | 2014 |
| Parallel coordinate descent methods for big data optimization P Richtárik, M Takáč Mathematical Programming, Series A, 1-52, 2015 | 455 | 2015 |
| Communication-efficient distributed dual coordinate ascent M Jaggi, V Smith, M Takáč, J Terhorst, S Krishnan, T Hofmann, MI Jordan arXiv preprint arXiv:1409.1458, 2014 | 319 | 2014 |
| 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 | 270 | 2017 |
| 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 | 233 | 2015 |
| Reinforcement learning for solving the vehicle routing problem M Nazari, A Oroojlooy, LV Snyder, M Takáč arXiv preprint arXiv:1802.04240, 2018 | 229 | 2018 |
| Distributed coordinate descent method for learning with big data P Richtárik, M Takác Journal of Machine Learning Research 17, 1-25, 2016 | 199 | 2016 |
| 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 | 191* | 2013 |
| 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 | 168 | 2015 |
| CoCoA: A general framework for communication-efficient distributed optimization V Smith, S Forte, M Chenxin, M Takáč, MI Jordan, M Jaggi Journal of Machine Learning Research 18, 230, 2018 | 149 | 2018 |
| On optimal probabilities in stochastic coordinate descent methods P Richtárik, M Takáč Optimization Letters, 2015, 1-11, 2015 | 103 | 2015 |
| 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 | 100 | 2017 |
| 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 | 97 | 2018 |
| 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 | 84 | 2016 |
| 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 | 71 | 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 | 60 | 2014 |
| Stochastic recursive gradient algorithm for nonconvex optimization LM Nguyen, J Liu, K Scheinberg, M Takáč arXiv preprint arXiv:1705.07261, 2017 | 59 | 2017 |
| Primal-Dual Rates and Certificates C Dünner, S Forte, M Takáč, M Jaggi In 33rd International Conference on Machine Learning, ICML 2016, 2016 | 57 | 2016 |
| Efficient serial and parallel coordinate descent methods for huge-scale truss topology design P Richtárik, M Takáč Operations Research Proceedings 2011, 27-32, 2012 | 55 | 2012 |
| Stochastic reformulations of linear systems: algorithms and convergence theory P Richtárik, M Takác SIAM Journal on Matrix Analysis and Applications 41 (2), 487-524, 2020 | 54 | 2020 |
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
