| Multi-task learning with deep neural networks: A survey M Crawshaw arXiv preprint arXiv:2009.09796, 2020 | 854 | 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 | 67 | 2022 |
| Fast Composite Optimization and Statistical Recovery in Federated Learning Y Bao, M Crawshaw, S Luo, M Liu International Conference on Machine Learning, 1508-1536, 2022 | 16 | 2022 |
| Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds M Crawshaw, Y Bao, M Liu Advances in Neural Information Processing Systems 36, 2024 | 13 | 2024 |
| EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data M Crawshaw, Y Bao, M Liu International Conference on Learning Representations, 2023 | 8 | 2023 |
| Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis M Crawshaw, M Liu arXiv preprint arXiv:2410.23131, 2024 | 1 | 2024 |
| Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective Y Bao, M Crawshaw, M Liu Forty-first International Conference on Machine Learning, 0 | | |
Mingrui LiuGeorge Mason UniversityVerified email at gmu.edu
