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We study the problem of choosing algorithm hyper-parameters in unsuperviseddomain adaptation, i.e., with labeled data in a source domain and unlabeleddata in a target domain, drawn from a different input distribution. We followthe strategy to compute several models using different hyper-parameters, and,to subsequently compute a linear aggregation of the models. While severalheuristics exist that follow this strategy, methods are still missing that relyon thorough theories for bounding the target error. In this turn, we propose amethod that extends weighted least squares to vector-valued functions, e.g.,deep neural networks. We show that the target error of the proposed algorithmis asymptotically not worse than twice the error of the unknown optimalaggregation. We also perform a large scale empirical comparative study onseveral datasets, including text, images, electroencephalogram, body sensorsignals and signals from mobile phones. Our method outperforms deep embeddedvalidation (DEV) and importance weighted validation (IWV) on all datasets,setting a new state-of-the-art performance for solving parameter choice issuesin unsupervised domain adaptation with theoretical error guarantees. We furtherstudy several competitive heuristics, all outperforming IWV and DEV on at leastfive datasets. However, our method outperforms each heuristic on at least fiveof seven datasets.

Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation