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Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation
[article]
2020
arXiv
pre-print
We consider representation learning (hypothesis class H = F∘G) where training and test distributions can be different. Recent studies provide hints and failure examples for domain invariant representation learning, a common approach for this problem, but the explanations provided are somewhat different and do not provide a unified picture. In this paper, we provide new decompositions of risk which give finer-grained explanations and clarify potential generalization issues. For Single-Source
arXiv:2004.10390v2
fatcat:u4hvbva6ejhirimfqjiv7g5pku