4 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 2 cited
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
Michael Crawshaw, Yajie Bao, Mingrui Liu
Gradient clipping is an important technique for deep neural networks with exploding gradients, such as recurrent neural networks. Recent studies have shown that the loss functions…
cs.LG2022★ 4 cited
Robustness to Unbounded Smoothness of Generalized SignSGD
Michael Crawshaw, Mingrui Liu, Francesco Orabona +2
Traditional analyses in non-convex optimization typically rely on the smoothness assumption, namely requiring the gradients to be Lipschitz. However, recent evidence shows that thi…
cs.LG2022★ 3 cited
Fast Composite Optimization and Statistical Recovery in Federated Learning
Yajie Bao, Michael Crawshaw, Shan Luo +1
As a prevalent distributed learning paradigm, Federated Learning (FL) trains a global model on a massive amount of devices with infrequent communication. This paper investigates a…