11 citations · 33 across the 8 of their papers we have counts for
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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★ 2 cited
An Information-Theoretic Approach to Transferability in Task Transfer Learning
Yajie Bao, Yang Li, Shao-Lun Huang +4
Task transfer learning is a popular technique in image processing applications that uses pre-trained models to reduce the supervision cost of related tasks. An important question i…
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…