Crawshaw, Michael

9 publications

ICLR 2025 Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-Convex Stochastic Optimization Under Relaxed Smoothness Michael Crawshaw, Mingrui Liu

ICML 2025 Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability Michael Crawshaw, Blake Woodworth, Mingrui Liu

ICLR 2025 Local Steps Speed up Local GD for Heterogeneous Distributed Logistic Regression Michael Crawshaw, Blake Woodworth, Mingrui Liu

NeurIPS 2024 Federated Learning Under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis Michael Crawshaw, Mingrui Liu

ICML 2024 Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective Yajie Bao, Michael Crawshaw, Mingrui Liu

ICLR 2023 EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data Michael Crawshaw, Yajie Bao, Mingrui Liu

NeurIPS 2023 Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds Michael Crawshaw, Yajie Bao, Mingrui Liu

ICML 2022 Fast Composite Optimization and Statistical Recovery in Federated Learning Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu

NeurIPS 2022 Robustness to Unbounded Smoothness of Generalized SignSGD Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang