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On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis. (arXiv:2205.04641v1 [cs.LG])
Web: http://arxiv.org/abs/2205.04641
cs.LG updates on arXiv.org arxiv.org
The establishment of the link between causality and unsupervised domain
adaptation (UDA)/semi-supervised learning (SSL) has led to methodological
advances in these learning problems in recent years. However, a formal theory
that explains the role of causality in the generalization performance of
UDA/SSL is still lacking. In this paper, we consider the UDA/SSL setting where
we access m labeled source data and n unlabeled target data as training
instances under a parametric probabilistic model. We study the learning
performance (e.g., excess …
analysis arxiv causality domain adaptation information learning on semi-supervised semi-supervised learning supervised learning