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[2602.14622] Tabular Foundation Models Can Learn Association Rules
Summary
This paper presents a model-agnostic framework for learning association rules using Tabular Foundation Models (TFMs), addressing limitations of traditional methods in low-data scenarios.
Why It Matters
The research highlights a significant advancement in association rule mining, particularly in high-stakes decision-making contexts. By leveraging TFMs, the framework can produce high-quality rules without the scalability issues of classical methods, making it relevant for data scientists and AI practitioners dealing with tabular data.
Key Takeaways
- Introduces a model-agnostic framework for association rule learning.
- Utilizes Tabular Foundation Models to enhance rule extraction.
- Demonstrates robustness in low-data environments without specific training.
- Evaluates performance across various tabular datasets.
- Provides source code for practical implementation.
Computer Science > Artificial Intelligence arXiv:2602.14622 (cs) [Submitted on 16 Feb 2026] Title:Tabular Foundation Models Can Learn Association Rules Authors:Erkan Karabulut, Daniel Daza, Paul Groth, Martijn C. Schut, Victoria Degeler View a PDF of the paper titled Tabular Foundation Models Can Learn Association Rules, by Erkan Karabulut and 4 other authors View PDF HTML (experimental) Abstract:Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues but suffer from degraded performance in low-data regimes. Tabular foundation models (TFMs), pretrained on diverse tabular data with strong in-context generalization, provide a basis for addressing these limitations. We introduce a model-agnostic association rule learning framework that extracts association rules from any conditional probabilistic model over tabular data, enabling us to leverage TFMs. We then introduce TabProbe, an instantiation of our framework that utilizes TFMs as conditional probability estimators to learn association rules out-of-the-box without frequent itemset mining. We evaluate our approach on tabular datasets of varying sizes based on standard ARM rule quality metrics and downstream classification performance. The results show that TFMs consistently produce conc...
