ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning

• URL: http://arxiv.org/abs/2608.13190v1
• Date: Thu, 13 Aug 2026 12:57:41 GMT
• Title: ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning
• Abstract summary: Group-robust learning is crucial for maintaining accuracy on rare subpopulations when training-group labels are unavailable.<n>We formulate group robustness without training-group labels as the endogenous environments.<n>We propose ProME to align both decisions with the deployed predictor.
• Score: 7.845234210436551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Group-robust learning is crucial for maintaining accuracy on rare subpopulations when training-group labels are unavailable. However, existing methods often infer environments from a separate reference model and select representations before fitting the classifier used at deployment, leaving both decisions misaligned with the deployed predictor. In this work, we formulate group robustness without training-group labels as the endogenous environments with repair-aware selection (ERAS) problem, and propose ProME (Prototype-Margin Environments) to align both decisions with the deployed predictor. ProME splits prototype margins at their median to construct approximately balanced environments along the training trajectory, and fits a group-balanced linear head on group-annotated validation data to rank the resulting predictors by validation worst-group accuracy. We theoretically bound the worst risk across the inferred environments for a fixed predictor and partition, showing that this bound transfers to the oracle groups under an explicit alignment condition. Extensive experiments show that prototype margins enrich shortcut-conflicting examples, classifier repair reshapes candidate evaluation, and ProME achieves the highest average worst-group accuracy among the compared methods with the same group-label access.

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