169 citations · 509 across the 21 of their papers we have counts for
3 papers · 1 filter
Causal Generative Domain Adaptation Networks
Mingming Gong, Kun Zhang, Biwei Huang +3
An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adap…
Transfer Learning with Label Noise
Xiyu Yu, Tongliang Liu, Mingming Gong +3
Transfer learning aims to improve learning in target domain by borrowing knowledge from a related but different source domain. To reduce the distribution shift between source and t…
Robust Learning via Cause-Effect Models
Bernhard Schölkopf, Dominik Janzing, Jonas Peters +1
We consider the problem of function estimation in the case where the data distribution may shift between training and test time, and additional information about it may be availabl…