5 papers
Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination
Seonghwi Kim, Sung Ho Jo, Minwoo Chae
Learning robust survival models under distribution shift is an important but challenging problem in many applications. In heterogeneous populations, a model that performs well on a…
Parameter-Efficient Distributionally Robust Adaptation of Tabular Foundation Models under Subpopulation Shift
Seonghwi Kim, Sung Ho Jo, Minwoo Chae
Despite strong mean accuracy, tabular foundation models (TFMs) can perform poorly on underrepresented groups under subpopulation shift, where group proportions change between train…
Optimal Transport Reweighting for Robust Learning under Spurious Correlations and Label Noise
Sung Ho Jo, Seonghwi Kim, Wonsang Yun +1
Machine learning models often suffer performance degradation under subpopulation shift, particularly when spurious correlations cause models to rely on shortcut features that fail…
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…
Mitigating Spurious Correlation via Distributionally Robust Learning with Hierarchical Ambiguity Sets
Sung Ho Jo, Seonghwi Kim, Minwoo Chae
Conventional supervised learning methods are often vulnerable to spurious correlations, particularly under distribution shifts in test data. To address this issue, several approach…