Tabular Foundation Models Can Learn Association Rules
The authors introduced a model-agnostic framework that allows any existing Tabular Foundation Model (TFM) to automatically learn association rules from tabular data without manual itemset mining. This method produces concise, high-quality rules with strong predictive performance, remains robust in low-data settings, and avoids the need for task-specific training.