| Reinforcement learning for solving the vehicle routing problem M Nazari, A Oroojlooy, LV Snyder, M Takáč Conference on Neural Information Processing Systems, NeurIPS 2018, 2018 | 870 | 2018 |
| 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 | 834 | 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 | 542 | 2017 |
| Parallel coordinate descent methods for big data optimization P Richtárik, M Takáč Mathematical Programming, Series A, 1-52, 2015 | 526 | 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 27, 2014 | 395 | 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 | 314 | 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 | 268 | 2018 |
| Distributed coordinate descent method for learning with big data P Richtárik, M Takác Journal of Machine Learning Research 17, 1-25, 2016 | 254 | 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 | 206* | 2013 |
| 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 | 199 | 2018 |
| 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 | 198 | 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 | 193 | 2017 |
| Distributed learning with compressed gradient differences K Mishchenko, E Gorbunov, M Takáč, P Richtárik arXiv preprint arXiv:1901.09269, 2019 | 184 | 2019 |
| 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 | 140 | 2016 |
| On optimal probabilities in stochastic coordinate descent methods P Richtárik, M Takáč Optimization Letters, 2015, 1-11, 2015 | 132 | 2015 |
| Applying deep learning to the newsvendor problem A Oroojlooyjadid, LV Snyder, M Takáč IISE Transactions 52 (4), 444-463, 2020 | 124 | 2020 |
| 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 | 109 | 2016 |
| Stochastic recursive gradient algorithm for nonconvex optimization LM Nguyen, J Liu, K Scheinberg, M Takáč arXiv preprint arXiv:1705.07261, 2017 | 98 | 2017 |
| 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 | 85 | 2020 |
| A deep q-network for the beer game: Deep reinforcement learning for inventory optimization A Oroojlooyjadid, MR Nazari, LV Snyder, M Takáč Manufacturing & Service Operations Management 24 (1), 285-304, 2022 | 84 | 2022 |
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
