Tabular Foundation Models Can Learn Association Rules
Amsterdam UMC, location VUmc, Vrije Universiteit Amsterdam, the Netherlands（阿姆斯特丹大学医学中心，位于VUmc，荷兰代尔夫特自由大学）；University of Amsterdam, The Netherlands（阿姆斯特丹大学，荷兰）；Vrije Universiteit Amsterdam, The Netherlands（荷兰阿姆斯特丹自由大学）
Erkan Karabulut, Daniel Daza, Paul Groth, Martijn C. Schut, Victoria Degeler
备注：
An extended version of this preprint, entitled Model-Agnostic Association Rule Learning with Tabular Foundation Models, is published in TMLR: https://openreview.net/pdf?id=Y70lnNnA6T
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 https://github.com/DiTEC-project/tabprobe.
Beyond IID: How General Are Tabular Foundation Models, Really?
Prior Labs；University of Freiburg（弗赖堡大学）；University of Mannheim（曼海姆大学）；INRIA Saclay（法国国家信息与自动化研究所萨克雷研究中心）；Technion（技术学院）；ELLIS Institute Tübingen（图宾根ELLIS研究所）；Zuse School ELIZA（Zuse学校ELIZA）
TabGift: A Context-Efficient Foundation Model for Tabular Data Generation
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
When Does Small Data Work? Accuracy and Efficiency Trade-offs Between Tabular Foundation Models and Conventional Methods for Crowd-State Classification at Hajj and Umrah
KnowsTFM: Knowledge-Informed Fine-Tuning of Small Tabular Foundation Models
Jožef Stefan Institute and Postgraduate School（乔塞夫·斯蒂芬研究所和研究生学院）；Department of Computer Science and Technology, University of Cambridge, UK（剑桥大学计算机科学与技术系）；Télécom Paris, Institut Polytechnique de Paris, FR（巴黎电信学院，巴黎理工学院）
Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models
Colegio de Ciencias e Ingenierías, Universidad San Francisco de Quito (USFQ)（基多圣弗朗西斯科大学科学与工程学院）；Rey Juan Carlos University（胡安·卡洛斯国王大学）；Facultad de Ingeniería, Universidad Latina de Panamá（巴拿马拉丁大学工程学院）
Generalization Can Emerge in Tabular Foundation Models From a Single Table
University of Toronto（多伦多大学）；Polytechnique Montréal（蒙特利尔大学）；Mila – Quebec AI Institute（魁北克人工智能研究所）；Layer 6 AI（Layer 6 AI公司）；Prior Labs（Prior实验室）；ELLIS Institute（ELLIS研究所）；Tübingen University of Freiburg（弗赖堡大学）
Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation
ADAPT Centre, Dublin City University（ADAPT中心，都柏林城市大学）；School of Computing, Dublin City University（都柏林城市大学计算机学院）；Department of Computer Science and Engineering, University of Bologna（博洛尼亚大学计算机科学与工程系）
LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究 institute）；Department of Computer Science and Artificial Intelligence (DECSAI)（计算机科学与人工智能系）；Research Center in Information and Communication Technologies (CITIC)（信息与通信技术研究中心）；Instituto de Investigación Biosanitaria Ibs.GRANADA, University of Granada, Granada, 18071, Spain（格拉纳达大学生物医学研究所）
