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

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

Record details

Published: 13 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment · Environment
Retrieved: 14 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (13 August 2026), “ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning,” evidence record 19474, https://ethics.ai/record/19474 (originally published by arXiv cs.LG).

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