Reliability-Aware Environment Discovery for Subpopulation Robustness

Machine learning models often fail under subpopulation shift, where subgroup proportions differ between training and test environments. In this setting, empirical risk minimization may rely on spurious correlations and underperform on minority subgroups. Existing group-robust methods address this issue by optimizing worst-group risk, but typically require subgroup annotations or rely on error-based environment discovery. These approaches identify vulnerable samples through observed prediction errors, overlooking correctly classified examples that may become misclassified when spurious cues change. To address this limitation, we propose Reliability-Aware Environment Discovery (RAE), an annotation-free framework that supplements prediction errors with margin-based uncertainty to discover candidate vulnerable environments. RAE employs twin discovery networks with reliability-guided label updates and auxiliary regularization, and combines prediction errors with uncertainty-based selection to construct environment partitions. Experiments on vision and language benchmarks demonstrate competitive worst-group performance. Further analyses show that prediction reliability complements error-based discovery by identifying currently correct but unreliable examples that would otherwise be excluded.