Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift

arXiv:2208.14161v3 Announce Type: replace
Abstract: Multi-source domain adaptation (MSDA) addresses the challenge of learning a label prediction function for an unlabeled target domain by leveraging both the labeled data from multiple source domains and the unlabeled data from the target domain. Conventional MSDA approaches often rely on covariate shift or conditional shift paradigms, which assume a consistent label distribution across domains. However, this assumption proves limiting in practical scenarios where label distributions do vary across domains, diminishing its applicability in …

abstract arxiv causal challenge cs.lg data domain domain adaptation domains function multiple prediction shift stat.ml type