Michael Crawshaw

According to our database, Michael Crawshaw authored at least 11 papers between 2020 and 2025.

Collaborative distances:

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

2025

Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability.

Michael Crawshaw, Blake Woodworth, Mingrui Liu

CoRR, June, 2025

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression.

Michael Crawshaw, Blake Woodworth, Mingrui Liu

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness.

Michael Crawshaw, Mingrui Liu

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

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

Michael Crawshaw, Mingrui Liu

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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