גילוי קבוצות נסתרות לסיום סיווג רובוסט

תקציר מקורי באנגליתarXiv:2606.23609v2 Announce Type: replace Abstract: Machine learning models exploit spurious correlations, achieving high average accuracy but failing disproportionately on underrepresented subgroups. Existing methods address this by adjusting network parameters, guided either by subgroup annotations or inferred pseudo-group labels. Yet at inference, these methods produce only a class prediction, with no insight into a sample's latent subgroup. We propose neural classification trees (NCT), a framework that achieves robustness by encoding subgroup structure in its tree-shaped architecture. By routing each sample to an "easy" or "hard" node of this tree - based on prediction correctness - and reusing these routes as pseudo-labels for the next iteration, NCT disentangles conflicting subgroups