| Even faster accelerated coordinate descent using non-uniform sampling Z Allen-Zhu, Z Qu, P Richtárik, Y Yuan International Conference on Machine Learning, 1110-1119, 2016 | 206 | 2016 |
| Coordinate descent with arbitrary sampling I: Algorithms and complexity Z Qu, P Richtárik Optimization Methods and Software 31 (5), 829-857, 2016 | 144 | 2016 |
| Quartz: Randomized dual coordinate ascent with arbitrary sampling Z Qu, P Richtárik, T Zhang Advances in neural information processing systems 28, 2015 | 139 | 2015 |
| SDNA: Stochastic dual Newton ascent for empirical risk minimization Z Qu, P Richtárik, M Takác, O Fercoq International Conference on Machine Learning, 1823-1832, 2016 | 109 | 2016 |
| Stochastic dual coordinate ascent with adaptive probabilities D Csiba, Z Qu, P Richtárik International Conference on Machine Learning, 674-683, 2015 | 105 | 2015 |
| Coordinate descent with arbitrary sampling II: Expected separable overapproximation Z Qu, P Richtárik Optimization Methods and Software 31 (5), 858-884, 2016 | 79 | 2016 |
| Semi-stochastic coordinate descent J Konečný, Z Qu, P Richtárik optimization Methods and Software 32 (5), 993-1005, 2017 | 78 | 2017 |
| Fast distributed coordinate descent for non-strongly convex losses O Fercoq, Z Qu, P Richtárik, M Takáč 2014 IEEE International Workshop on Machine Learning for Signal Processing …, 2014 | 76 | 2014 |
| Curse of dimensionality reduction in max-plus based approximation methods: Theoretical estimates and improved pruning algorithms S Gaubert, W McEneaney, Z Qu 2011 50th IEEE Conference on Decision and Control and European Control …, 2011 | 67 | 2011 |
| Restarting accelerated gradient methods with a rough strong convexity estimate O Fercoq, Z Qu arXiv preprint arXiv:1609.07358, 2016 | 62 | 2016 |
| Adaptive restart of accelerated gradient methods under local quadratic growth condition O Fercoq, Z Qu IMA Journal of Numerical Analysis 39 (4), 2069-2095, 2019 | 57 | 2019 |
| L-SVRG and L-Katyusha with arbitrary sampling X Qian, Z Qu, P Richtárik Journal of Machine Learning Research 22 (112), 1-47, 2021 | 35 | 2021 |
| SAGA with arbitrary sampling X Qian, Z Qu, P Richtárik International Conference on Machine Learning, 5190-5199, 2019 | 27 | 2019 |
| Dobrushin’s ergodicity coefficient for Markov operators on cones S Gaubert, Z Qu Integral Equations and Operator Theory 81 (1), 127-150, 2015 | 27 | 2015 |
| Restarting the accelerated coordinate descent method with a rough strong convexity estimate O Fercoq, Z Qu Computational Optimization and Applications 75, 63-91, 2020 | 22 | 2020 |
| The contraction rate in Thompson's part metric of order-preserving flows on a cone–Application to generalized Riccati equations S Gaubert, Z Qu Journal of Differential Equations 256 (8), 2902-2948, 2014 | 19 | 2014 |
| Contraction of Riccati Flows Applied to the Convergence Analysis of a Max-Plus Curse-of-Dimensionality--Free Method Z Qu SIAM Journal on Control and Optimization 52 (5), 2677-2706, 2014 | 18 | 2014 |
| Checking strict positivity of Kraus maps is NP-hard S Gaubert, Z Qu Information Processing Letters 118, 35-43, 2017 | 10 | 2017 |
| S2cd: Semi-stochastic coordinate descent J Konecný, Z Qu, P Richtárik NIPS Optimization in Machine Learning workshop, 2014 | 10 | 2014 |
| An inexact proximal augmented Lagrangian framework with arbitrary linearly convergent inner solver for composite convex optimization F Li, Z Qu Mathematical Programming Computation 13, 583-644, 2021 | 9 | 2021 |
