Neural Classification Trees

Neural Classification Trees (NCT) discover latent subgroups for robust classification without group supervision

A new arXiv preprint introduces Neural Classification Trees (NCT), a framework that encodes subgroup structure in a tree-shaped architecture to address spurious correlations in ML classifiers. By routing samples to 'easy' or 'hard' nodes based on prediction correctness and reusing routes as pseudo-labels iteratively, NCT disentangles conflicting subgroups without requiring subgroup annotations. The method is evaluated on five benchmarks covering binary and multi-class spurious correlations, achieving competitive robustness while providing interpretable mappings between model architecture and latent data structure.