Discovering Latent Groups for Robust Classification

arXiv·low signal

ML models exploit spurious correlations to achieve high average accuracy while failing disproportionately on underrepresented subgroups; this work discovers latent groups to improve worst-case robustness without explicit group labels. Relevant for builders worried about silent failure on minority slices of their data where aggregate metrics look fine.