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 w...