3 papers
cs.LG2026
Distributionally Robust Classification for Multi-source Unsupervised Domain Adaptation
Seonghwi Kim, Sung Ho Jo, Wooseok Ha +1
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…
stat.ML2025
When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts
Wooseok Ha, Yuansi Chen
Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled targe…
stat.ML2023
Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms
Keru Wu, Yuansi Chen, Wooseok Ha +1
Domain adaptation (DA) is a statistical learning problem that arises when the distribution of the source data used to train a model differs from that of the target data used to eva…