A Theory of Multiple-Source Adaptation with Limited Target Labeled Data

TLDR

This work analyzes existing algorithms for multiple-source domain adaptation, and proposes a novel algorithm based on model selection that is efficient, and experiments on real data-sets empirically demonstrate their benefits.

Abstract

We study multiple-source domain adaptation, when the learner has access to abundant labeled data from multiple-source domains and limited labeled data from the target domain. We analyze existing algorithms for this problem, and propose a novel algorithm based on model selection. Our algorithms are efficient, and experiments on real data-sets empirically demonstrate their benefits.