Multi-Task Learning with Deep Neural Networks: A Survey. - Michael Crawshaw (CoRR, 2020)

SLAW: Scaled Loss Approximate Weighting for Efficient Multi-Task Learning. - Michael Crawshaw; Jana Kosecká (CoRR, 2021)

Fast Composite Optimization and Statistical Recovery in Federated Learning. - Yajie Bao; Michael Crawshaw; Shan Luo 005; Mingrui Liu (ICML, 2022)

Robustness to Unbounded Smoothness of Generalized SignSGD. - Michael Crawshaw; Mingrui Liu; Francesco Orabona; Wei Zhang 022; Zhenxun Zhuang (NeurIPS, 2022)

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

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

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

Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis. - Michael Crawshaw; Mingrui Liu (NeurIPS, 2024)

Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness. - Michael Crawshaw; Mingrui Liu (ICLR, 2025)

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression. - Michael Crawshaw; Blake Woodworth; Mingrui Liu (ICLR, 2025)

Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability. - Michael Crawshaw; Blake Woodworth; Mingrui Liu (CoRR, 2025)