| Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning H Rafique, M Liu, Q Lin, T Yang Optimization Methods and Software 37 (3), 1087-1121, 2022 | 315 | 2022 |
| First-order convergence theory for weakly-convex-weakly-concave min-max problems M Liu, H Rafique, Q Lin, T Yang Journal of Machine Learning Research 22 (169), 1-34, 2021 | 144* | 2021 |
| Understanding adamw through proximal methods and scale-freeness Z Zhuang, M Liu, A Cutkosky, F Orabona arXiv preprint arXiv:2202.00089, 2022 | 139 | 2022 |
| Stochastic AUC Maximization with Deep Neural Networks M Liu, Z Yuan, Y Ying, T Yang International Conference on Learning Representations 2020, 2019 | 125 | 2019 |
| Improved Schemes for Episodic Memory-based Lifelong Learning Y Guo*, M Liu*, T Yang, T Rosing Advances in Neural Information Processing Systems 33, 2020 | 120 | 2020 |
| Robustness to unbounded smoothness of generalized signsgd M Crawshaw, M Liu, F Orabona, W Zhang, Z Zhuang Advances in neural information processing systems 35, 9955-9968, 2022 | 117 | 2022 |
| A decentralized parallel algorithm for training generative adversarial nets M Liu, W Zhang, Y Mroueh, X Cui, J Ross, T Yang, P Das Advances in Neural Information Processing Systems 33, 11056-11070, 2020 | 99 | 2020 |
| Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets M Liu, Y Mroueh, J Ross, W Zhang, X Cui, P Das, T Yang International Conference on Learning Representations 2020, 2019 | 87 | 2019 |
| Fast Stochastic AUC Maximization with -Convergence Rate M Liu, X Zhang, Z Chen, X Wang, T Yang International Conference on Machine Learning, 3189-3197, 2018 | 76 | 2018 |
| ADMM without a fixed penalty parameter: Faster convergence with new adaptive penalization Y Xu, M Liu, Q Lin, T Yang Advances in neural information processing systems 30, 2017 | 73 | 2017 |
| Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks Z Guo, M Liu, Z Yuan, L Shen, W Liu, T Yang International Conference on Machine Learning 2020, 2020 | 54 | 2020 |
| Will bilevel optimizers benefit from loops K Ji, M Liu, Y Liang, L Ying Advances in Neural Information Processing Systems 35, 3011-3023, 2022 | 52 | 2022 |
| Adam: A Stochastic Method with Adaptive Variance Reduction M Liu, W Zhang, F Orabona, T Yang arXiv preprint arXiv:2011.11985, 2020 | 47 | 2020 |
| A communication-efficient distributed gradient clipping algorithm for training deep neural networks M Liu, Z Zhuang, Y Lei, C Liao Advances in Neural Information Processing Systems 35, 26204-26217, 2022 | 46 | 2022 |
| Generalization guarantee of SGD for pairwise learning Y Lei, M Liu, Y Ying Advances in neural information processing systems 34, 21216-21228, 2021 | 45 | 2021 |
| Adaptive negative curvature descent with applications in non-convex optimization M Liu, Z Li, X Wang, J Yi, T Yang Advances in Neural Information Processing Systems, 4853-4862, 2018 | 43* | 2018 |
| Bilevel coreset selection in continual learning: A new formulation and algorithm J Hao, K Ji, M Liu Advances in Neural Information Processing Systems 36, 51026-51049, 2023 | 42 | 2023 |
| Adaptive accelerated gradient converging methods under holderian error bound condition M Liu, T Yang Advances in Neural Information Processing Systems 30, 2016 | 31 | 2016 |
| Spatiotemporal dynamics in a network composed of neurons with different excitabilities and excitatory coupling WW Xiao, HG Gu, MR Liu Science China Technological Sciences 59 (12), 1943-1952, 2016 | 30 | 2016 |
| Fast rates of erm and stochastic approximation: Adaptive to error bound conditions M Liu, X Zhang, L Zhang, R Jin, T Yang Advances in Neural Information Processing Systems 30, 2018 | 29 | 2018 |
Tianbao YangProfessor, Stephen Horn '79 Chair, Texas A&M UniversityVerified email at tamu.edu
