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Co-regularization Based Semi-supervised Domain Adaptation
Hal Daumé III · Abhishek Kumar · Avishek Saha

Mon Dec 06 12:00 AM -- 12:00 AM (PST) @ None #None

This paper presents a co-regularization based approach to semi-supervised domain adaptation. Our proposed approach (EA++) builds on the notion of augmented space (introduced in EASYADAPT (EA) [1]) and harnesses unlabeled data in target domain to further enable the transfer of information from source to target. This semi-supervised approach to domain adaptation is extremely simple to implement and can be applied as a pre-processing step to any supervised learner. Our theoretical analysis (in terms of Rademacher complexity) of EA and EA++ show that the hypothesis class of EA++ has lower complexity (compared to EA) and hence results in tighter generalization bounds. Experimental results on sentiment analysis tasks reinforce our theoretical findings and demonstrate the efficacy of the proposed method when compared to EA as well as a few other baseline approaches.

Author Information

Hal Daumé III (Univ of Maryland / Microsoft Research)
Abhishek Kumar (University of Maryland, College Park)
Avishek Saha (Yahoo Labs)

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