Unsupervised Multi-Source Domain Adaptation for Regression

Guillaume Richard (1, 2) , Antoine de Mathelin (1) , Georges Hébrail (2, 3) , Mathilde Mougeot (1, 4) , Nicolas Vayatis (1)

1 CB - CB - Centre Borelli - UMR 9010
2 EDF R&D - EDF R&D
3 IRT SystemX
4 ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise

We consider the problem of unsupervised domain adaptation from multiple sources in a regression setting. We propose in this work an original method to take benefit of different sources using a weighted combination of the sources. For this purpose, we define a new measure of similarity between probabilities for domain adaptation which we call hypothesis-discrepancy. We then prove a new bound for unsupervised domain adaptation combining multiple sources. We derive from this bound a novel adversarial domain adaptation algorithm adjusting weights given to each source, ensuring that sources related to the target receive higher weights. We finally evaluate our method on different public datasets and compare it to other domain adaptation baselines to demonstrate the improvement for regression tasks.

Domaines: Machine Learning [stat.ML]

Guillaume Richard, Antoine de Mathelin, Georges Hébrail, Mathilde Mougeot, Nicolas Vayatis. Unsupervised Multi-Source Domain Adaptation for Regression. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent, Belgium. pp.395-411, ⟨10.1007/978-3-030-67658-2_23⟩. ⟨hal-02543790⟩