AI RESEARCH
Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models
arXiv CS.LG
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ArXi:2604.13332v1 Announce Type: new Identifying meaningful feature interactions is a central challenge in building accurate and interpretable models for tabular data. Generalized additive models (GAMs) have shown great success at modeling tabular data, but often rely on heuristic procedures to select interactions, potentially missing higher-order or context-dependent effects. To meet this challenge, we propose TabDistill, a method that leverages tabular foundation models and post-hoc distillation methods.