Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation

Understand

Empirical risk minimization often performs poorly when the distribution of the target domain differs from those of source domains.

• To address such potential distribution shifts, we develop an unsupervised domain adaptation approach that leverages labeled data from multiple source domains and unlabeled data from the target domain.
• We introduce a distributionally robust model that optimizes an adversarial reward based on the explained variance across a class of target distributions, ensuring generalization to the target domain.
• We show that the proposed robust model is a weighted average of conditional outcome models from source domains.