Distributionally Robust Domain Adaptation
This work addresses robustness and generalization issues in domain adaptation for machine learning applications, representing an incremental improvement over prior methods.
The paper tackles the problem of domain adaptation models being vulnerable to noise and unable to generalize to unseen target samples by proposing DRDA, a distributionally robust method that minimizes worst-case target domain risk using a DRO framework with MMD, and it outperforms existing robust approaches in experiments.
Domain Adaptation (DA) has recently received significant attention due to its potential to adapt a learning model across source and target domains with mismatched distributions. Since DA methods rely exclusively on the given source and target domain samples, they generally yield models that are vulnerable to noise and unable to adapt to unseen samples from the target domain, which calls for DA methods that guarantee the robustness and generalization of the learned models. In this paper, we propose DRDA, a distributionally robust domain adaptation method. DRDA leverages a distributionally robust optimization (DRO) framework to learn a robust decision function that minimizes the worst-case target domain risk and generalizes to any sample from the target domain by transferring knowledge from a given labeled source domain sample. We utilize the Maximum Mean Discrepancy (MMD) metric to construct an ambiguity set of distributions that provably contains the source and target domain distributions with high probability. Hence, the risk is shown to upper bound the out-of-sample target domain loss. Our experimental results demonstrate that our formulation outperforms existing robust learning approaches.