I am an assistant professor at Department of Computer Science, George Mason University. Before that I was a postdoc at Rafik B. Hariri Institute at Boston University, hosted by Francesco Orabona. I received my Ph.D. at Department of Computer Science, The University of Iowa in August 2020, under the advise of Tianbao Yang. Before that I studied at Institute of Natural Sciences and School of Mathematical Sciences at Shanghai Jiao Tong University. I have also spent time working at industrial research labs, such as IBM research AI and Alibaba DAMO Academy.
Selected Publications:
1. Fast Composite Optimization and Statistical Recovery in Federated Learning.
Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu.
Proceedings of 39th International Conference on Machine Learning, 2022. (ICML 2022)
2. First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems.
Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang.
Journal of Machine Learning Research, 2021. (JMLR 2021)
3. A Decentralized Parallel Algorithm for Training Generative Adversarial Nets.
Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das.
Advances in Neural Information Processing Systems 33, 2020. (NeurIPS 2020)
4. Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets.
Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang.
8th International Conference on Learning Representations, 2020. (ICLR 2020)
5. Stochastic AUC Maximization with Deep Neural Networks.
Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang.
8th International Conference on Learning Representations, 2020. (ICLR 2020)
6. Fast Stochastic AUC Maximization with O(1/n) Convergence Rate.
Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang.
Proceedings of the 35th International Conference on Machine Learning 35, 2018. (ICML 2018)