Rank-Constrained Adaptation for Reliable Real-World Performance

Abstract:Deep learning models trained to optimize average accuracy often exhibit systematic failures on particular subpopulations. In real-world settings like healthcare, the subpopulations most affected by such disparities are frequently unlabeled, partially observed, or not known in advance. Existing group-robust methods typically assume prior knowledge of the relevant subgroups, using group annotations for training, validation, or model selection. We propose Misclassification Aware Rank-Limited Adaptation (MARLA), a parameter-efficient method for improving worst group performance without explicit subgroup annotations. MARLA leverages an ERM-trained model by calculating the model's misclassification probability scores on a held-out adaptation set to identify a low-dimensional subspace where errors concentrate. We then learn a rank-restricted additive correction to the classifier logits within that subspace. Across seven real-world datasets, we evaluate group robustness under three settings: no knowledge of subgroup relevance, partial knowledge of subgroup relevance, and full knowledge of subgroup relevance. MARLA improves worst-group performance while remaining fast and parameter-efficient, with data-guided hyperparameter selection.

Machine Learning (cs.LG)

arXiv:2602.06924 [cs.LG]

(or arXiv:2602.06924v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.06924 Focus to learn more arXiv-issued DOI via DataCite

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