Michael Crawshaw

According to our database, Michael Crawshaw authored at least 8 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis.

Michael Crawshaw, Mingrui Liu

CoRR, 2024

Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective.

Yajie Bao, Michael Crawshaw, Mingrui Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds.

Michael Crawshaw, Yajie Bao, Mingrui Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data.

Michael Crawshaw, Yajie Bao, Mingrui Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Robustness to Unbounded Smoothness of Generalized SignSGD.

Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fast Composite Optimization and Statistical Recovery in Federated Learning.

Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu

Proceedings of the International Conference on Machine Learning, 2022

2021

SLAW: Scaled Loss Approximate Weighting for Efficient Multi-Task Learning.

Michael Crawshaw, Jana Kosecká

CoRR, 2021

2020

Multi-Task Learning with Deep Neural Networks: A Survey.

Michael Crawshaw

CoRR, 2020