| Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems L Luo, H Ye, Z Huang, T Zhang Advances in Neural Information Processing Systems (NeurIPS), 2020 | 154 | 2020 |
| Support matrix machines L Luo, Y Xie, Z Zhang, WJ Li International Conference on Machine Learning (ICML), 2015 | 150 | 2015 |
| Multi-Consensus Decentralized Accelerated Gradient Descent H Ye, L Luo, Z Zhou, T Zhang Journal of Machine Learning Research (JMLR) 24 (306), 1-50, 2023 | 80 | 2023 |
| SPSD matrix approximation vis column selection: theories, algorithms, and extensions S Wang, L Luo, Z Zhang Journal of Machine Learning Research (JMLR) 17 (1), 1697-1745, 2016 | 56* | 2016 |
| Decentralized Accelerated Proximal Gradient Descent H Ye, Z Zhou, L Luo, T Zhang Advances in Neural Information Processing Systems (NeurIPS), 2020 | 42 | 2020 |
| Finding second-order stationary points in nonconvex-strongly-concave minimax optimization L Luo, Y Li, C Chen Advances in Neural Information Processing Systems (NeurIPS), 2022 | 39 | 2022 |
| Approximate Newton Methods H Ye, L Luo, Z Zhang Journal of Machine Learning Research (JMLR) 22 (66), 1-41, 2021 | 33* | 2021 |
| Approximate Newton methods and their local convergence H Ye, L Luo, Z Zhang International Conference on Machine Learning (ICML), 2017 | 30 | 2017 |
| Robust frequent directions with application in online learning L Luo, C Chen, Z Zhang, WJ Li, T Zhang Journal of Machine Learning Research (JMLR) 20 (45), 1-41, 2019 | 29 | 2019 |
| Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems G Xie, L Luo, Y Lian, Z Zhang International Conference on Machine Learning (ICML), 2020 | 26 | 2020 |
| Quasi-Newton Methods for Saddle Point Problems C Liu, L Luo Advances in Neural Information Processing Systems (NeurIPS), 2022 | 25* | 2022 |
| Frequent direction algorithms for approximate matrix multiplication with applications in CCA Q Ye, L Luo, Z Zhang International Joint Conference on Artificial Intelligence (IJCAI), 2016 | 25 | 2016 |
| Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization L Luo, G Xie, T Zhang, Z Zhang arXiv preprint arXiv:2106.01761, 2021 | 24 | 2021 |
| Accelerating inexact hypergradient descent for bilevel optimization H Yang, L Luo, CJ Li, M Jordan, M Fazel OPT 2023: Optimization for Machine Learning, 2023 | 23 | 2023 |
| Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization L Chen, J Xu, L Luo International Conference on Machine Learning (ICML), 2023 | 23 | 2023 |
| Nesterov's Acceleration for Approximate Newton H Ye, L Luo, Z Zhang Journal of Machine Learning Research (JMLR) 21 (142), 1-37, 2020 | 23 | 2020 |
| Faster Stochastic Algorithms for Minimax Optimization under Polyak-Łojasiewicz Condition L Chen, B Yao, L Luo Advances in Neural Information Processing Systems (NeurIPS), 2022 | 21 | 2022 |
| Quasi-Newton Hamiltonian Monte Carlo. T Fu, L Luo, Z Zhang Conference on Uncertainty in Artificial Intelligence (UAI), 2016 | 20 | 2016 |
| Efficient and Robust High-Dimensional Linear Contextual Bandits C Chen, L Luo, W Zhang, Y Yu, Y Lian International Joint Conference on Artificial Intelligence (IJCAI), 2020 | 16 | 2020 |
| An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization L Chen, H Ye, L Luo International Conference on Artificial Intelligence and Statistics (AISTATS), 2024 | 15 | 2024 |
Zhihua ZhangProfessor of Computer Science, Shanghai Jiao Tong UniversityVerified email at zju.edu.cn
