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 | 148 | 2016 |

Coordinate descent with arbitrary sampling I: Algorithms and complexity Z Qu, P Richtárik Optimization Methods and Software 31 (5), 829-857, 2016 | 118 | 2016 |

Stochastic dual coordinate ascent with adaptive probabilities D Csiba, Z Qu, P Richtárik International Conference on Machine Learning, 674-683, 2015 | 94 | 2015 |

Quartz: Randomized dual coordinate ascent with arbitrary sampling Z Qu, P Richtárik, T Zhang Advances in neural information processing systems 28, 865-873, 2015 | 92 | 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 | 83 | 2016 |

Coordinate descent with arbitrary sampling II: Expected separable overapproximation Z Qu, P Richtárik Optimization Methods and Software 31 (5), 858-884, 2016 | 70 | 2016 |

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 | 63 | 2014 |

Semi-stochastic coordinate descent J Konečný, Z Qu, P Richtárik optimization Methods and Software 32 (5), 993-1005, 2017 | 47 | 2017 |

Restarting accelerated gradient methods with a rough strong convexity estimate O Fercoq, Z Qu arXiv preprint arXiv:1609.07358, 2016 | 47 | 2016 |

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 | 47 | 2011 |

Randomized dual coordinate ascent with arbitrary sampling Z Qu, P Richtárik, T Zhang arXiv preprint arXiv:1411.5873, 2014 | 35 | 2014 |

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 | 33 | 2019 |

Dobrushin’s ergodicity coefficient for Markov operators on cones S Gaubert, Z Qu Integral Equations and Operator Theory 81 (1), 127-150, 2015 | 18 | 2015 |

Restarting the accelerated coordinate descent method with a rough strong convexity estimate O Fercoq, Z Qu Computational Optimization and Applications 75 (1), 63-91, 2020 | 16 | 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 | 15 | 2014 |

L-SVRG and L-Katyusha with arbitrary sampling X Qian, Z Qu, P Richtárik Journal of Machine Learning Research 22 (112), 1-47, 2021 | 13 | 2021 |

SAGA with arbitrary sampling X Qian, Z Qu, P Richtárik International Conference on Machine Learning, 5190-5199, 2019 | 12 | 2019 |

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 | 12 | 2014 |

S2cd: Semi-stochastic coordinate descent J Konecný, Z Qu, P Richtárik NIPS Optimization in Machine Learning workshop, 2014 | 11 | 2014 |

A max-plus based randomized algorithm for solving a class of HJB PDEs Z Qu 53rd IEEE Conference on Decision and Control, 1575-1580, 2014 | 7 | 2014 |