Web: http://arxiv.org/abs/2205.04641

May 11, 2022, 1:11 a.m. | Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu

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

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