5 citations · 14 across the 10 of their papers we have counts for
4 papers · 1 filter
FedL2P: Federated Learning to Personalize
Royson Lee, Minyoung Kim, Da Li +4
Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those comm…
Better Practices for Domain Adaptation
Linus Ericsson, Da Li, Timothy M. Hospedales
Distribution shifts are all too common in real-world applications of machine learning. Domain adaptation (DA) aims to address this by providing various frameworks for adapting mode…
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach
Minyoung Kim, Da Li, Timothy Hospedales
We tackle the domain generalisation (DG) problem by posing it as a domain adaptation (DA) task where we adversarially synthesise the worst-case target domain and adapt a model to t…
Attacking Adversarial Defences by Smoothing the Loss Landscape
Panagiotis Eustratiadis, Henry Gouk, Da Li +1
This paper investigates a family of methods for defending against adversarial attacks that owe part of their success to creating a noisy, discontinuous, or otherwise rugged loss la…