Distributionally Robust Classification for Multi-source Unsupervised Domain Adaptation
This addresses domain adaptation challenges for machine learning practitioners, offering a versatile solution that is incremental in improving robustness.
The paper tackles the problem of unsupervised domain adaptation (UDA) struggling with limited target data or spurious correlations by proposing a distributionally robust learning framework, resulting in consistent outperformance of baselines, especially with extremely scarce target data.
Unsupervised domain adaptation (UDA) is a statistical learning problem when the distribution of training (source) data is different from that of test (target) data. In this setting, one has access to labeled data only from the source domain and unlabeled data from the target domain. The central objective is to leverage the source data and the unlabeled target data to build models that generalize to the target domain. Despite its potential, existing UDA approaches often struggle in practice, particularly in scenarios where the target domain offers only limited unlabeled data or spurious correlations dominate the source domain. To address these challenges, we propose a novel distributionally robust learning framework that models uncertainty in both the covariate distribution and the conditional label distribution. Our approach is motivated by the multi-source domain adaptation setting but is also directly applicable to the single-source scenario, making it versatile in practice. We develop an efficient learning algorithm that can be seamlessly integrated with existing UDA methods. Extensive experiments under various distribution shift scenarios show that our method consistently outperforms strong baselines, especially when target data are extremely scarce.