M. Schut

Tabular Foundation Models Can Learn Association Rules

This work introduces a model-agnostic association rule learning framework that extracts association rules from any conditional probabilistic model over tabular data, and introduces TabProbe, an instantiation of this framework that utilizes TFMs as conditional probability estimators to learn association rules out-of-the...