| 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 | 777 | 2014 |
| 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 | 682 | 2018 |
| Parallel coordinate descent methods for big data optimization P Richtárik, M Takáč Mathematical Programming, Series A, 1-52, 2015 | 511 | 2015 |
| 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 | 447 | 2017 |
| 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 | 371 | 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 | 283 | 2015 |
| Distributed coordinate descent method for learning with big data P Richtárik, M Takác Journal of Machine Learning Research 17, 1-25, 2016 | 237 | 2016 |
| 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 | 234 | 2018 |
| 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 | 200* | 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 | 183 | 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 | 174 | 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 | 160 | 2018 |
| Distributed learning with compressed gradient differences K Mishchenko, E Gorbunov, M Takáč, P Richtárik arXiv preprint arXiv:1901.09269, 2019 | 140 | 2019 |
| On optimal probabilities in stochastic coordinate descent methods P Richtárik, M Takáč Optimization Letters, 2015, 1-11, 2015 | 124 | 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 | 100 | 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 | 99 | 2016 |
| Applying deep learning to the newsvendor problem A Oroojlooyjadid, LV Snyder, M Takáč IISE Transactions 52 (4), 444-463, 2020 | 93 | 2020 |
| Stochastic recursive gradient algorithm for nonconvex optimization LM Nguyen, J Liu, K Scheinberg, M Takáč arXiv preprint arXiv:1705.07261, 2017 | 86 | 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 | 74 | 2020 |
| 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 | 74 | 2014 |
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
