表格基础模型能学会关联规则：TabProbe 框架发布

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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 concise, high-quality association rules with strong predictive performance and remain robust in low-data settings without task-specific training. Source code is available at this https URL.

Comments:	An extended version of this preprint, entitled Model-Agnostic Association Rule Learning with Tabular Foundation Models, is published in TMLR: this https URL
Subjects:	Artificial Intelligence (cs.AI); Databases (cs.DB); Machine Learning (cs.LG)
Cite as:	arXiv:2602.14622 [cs.AI]
	(or arXiv:2602.14622v3 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2602.14622 arXiv-issued DOI via DataCite
Journal reference:	Transactions on Machine Learning Research, 2026

Submission history

From: Erkan Karabulut [view email]
[v1] Mon, 16 Feb 2026 10:25:55 UTC (4,692 KB)
[v2] Tue, 17 Feb 2026 09:54:33 UTC (4,692 KB)
[v3] Tue, 20 Oct 2026 08:32:40 UTC (4,692 KB)