| 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 | 215 | 2022 |
| First-order convergence theory for weakly-convex-weakly-concave min-max problems M Liu, H Rafique, Q Lin, T Yang The Journal of Machine Learning Research 22 (1), 7651-7684, 2021 | 101* | 2021 |
| Stochastic AUC Maximization with Deep Neural Networks M Liu, Z Yuan, Y Ying, T Yang International Conference on Learning Representations 2020, 2019 | 74 | 2019 |
| 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 | 61 | 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 | 60 | 2018 |
| Improved Schemes for Episodic Memory-based Lifelong Learning Y Guo*, M Liu*, T Yang, T Rosing Advances in Neural Information Processing Systems 33, 2020 | 58* | 2020 |
| 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 | 54 | 2020 |
| 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 | 53 | 2017 |
| 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 | 39* | 2018 |
| 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 | 35 | 2020 |
| 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, 1943-1952, 2016 | 27 | 2016 |
| Adaptive accelerated gradient converging methods under holderian error bound condition M Liu, T Yang Advances in Neural Information Processing Systems 30, 2016 | 25 | 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 | 19 | 2018 |
| Improving efficiency in large-scale decentralized distributed training W Zhang, X Cui, A Kayi, M Liu, U Finkler, B Kingsbury, G Saon, Y Mroueh, ... ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 15 | 2020 |
| Adam: A Stochastic Method with Adaptive Variance Reduction M Liu, W Zhang, F Orabona, T Yang arXiv preprint arXiv:2011.11985, 2020 | 14 | 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 | 13 | 2022 |
| Understanding adamw through proximal methods and scale-freeness Z Zhuang, M Liu, A Cutkosky, F Orabona arXiv preprint arXiv:2202.00089, 2022 | 13 | 2022 |
| Generalization guarantee of SGD for pairwise learning Y Lei, M Liu, Y Ying Advances in Neural Information Processing Systems 34, 21216-21228, 2021 | 13 | 2021 |
| 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 | 10 | 2022 |
| Non-convex min–max optimization: provable algorithms and applications in machine learning (2018) H Rafique, M Liu, Q Lin, T Yang arXiv preprint arXiv:1810.02060, 1810 | 10 | 1810 |
Tianbao YangTexas A&M University在 tamu.edu 的电子邮件经过验证
