Distilling Tabular Foundation Models for Structured Health Data

In three linesTabular foundation models (TFMs) for healthcare are distilled into lightweight models using context-leakage-aware distillation. Across 19 medical datasets and 6 TFMs, student models retain ≥90% of teacher AUC while running 26× faster on CPU, preserving calibration and fairness critical for health applications.

Summary generated by Claude — human-verified