auc-6c0d7395·1 events·first seen Aliases: AUC
This paper investigates knowledge distillation from tabular foundation models (TFMs) to lightweight student models for healthcare applications. The authors address context leakage in in-context TFMs via stratified out-of-fold teacher labeling, evaluating across 19 healthcare datasets, 6 TFM teachers, and 4 student families. Distilled students retain at least 90% of teacher AUC while running 26× faster on CPU, with preserved calibration and fairness properties. Multi-teacher ensembles do not consistently outperform the best single teacher.