Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
This addresses a critical issue for researchers and practitioners in machine learning who need reliable hyper-parameter tuning in domain adaptation, though it is incremental as it builds on existing aggregation strategies with theoretical improvements.
The paper tackles the problem of hyper-parameter selection in unsupervised domain adaptation by proposing a method that aggregates models with different hyper-parameters using weighted least squares extended to vector-valued functions, showing it outperforms existing methods like DEV and IWV on all datasets and sets new state-of-the-art performance with theoretical error guarantees.
We study the problem of choosing algorithm hyper-parameters in unsupervised domain adaptation, i.e., with labeled data in a source domain and unlabeled data in a target domain, drawn from a different input distribution. We follow the strategy to compute several models using different hyper-parameters, and, to subsequently compute a linear aggregation of the models. While several heuristics exist that follow this strategy, methods are still missing that rely on thorough theories for bounding the target error. In this turn, we propose a method that extends weighted least squares to vector-valued functions, e.g., deep neural networks. We show that the target error of the proposed algorithm is asymptotically not worse than twice the error of the unknown optimal aggregation. We also perform a large scale empirical comparative study on several datasets, including text, images, electroencephalogram, body sensor signals and signals from mobile phones. Our method outperforms deep embedded validation (DEV) and importance weighted validation (IWV) on all datasets, setting a new state-of-the-art performance for solving parameter choice issues in unsupervised domain adaptation with theoretical error guarantees. We further study several competitive heuristics, all outperforming IWV and DEV on at least five datasets. However, our method outperforms each heuristic on at least five of seven datasets.