Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
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Abstract
Current methods for covariate-shift adaptation use unlabelled data to compute
importance weights or domain-invariant features, while the final model is
trained on labelled data only. Here, we consider a particular case of covariate
shift which allows us also to learn from unlabelled data, that is, combining
adaptation with semi-supervised learning. Using ideas from causality, we argue
that this requires learning with both causes,