José Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan
备注：
6 pages, 2 figures, 2 tables. Accepted at ESANN 2026 (European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning), 22-24 April 2026, Bruges (Belgium)
发表信息：
ESANN 2026 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online event, 22-24 April 2026, pp. 115-120, i6doc.com publ., ISBN 9782875870964
Recent Tabular Foundation Models (TFMs) have demonstrated state-of-the-art predictive performance, often surpassing Gradient-Boosted Decision Trees (GBDTs). However, the trustworthiness of these models, particularly their uncertainty quantification, has been largely overlooked. We investigate this gap through an extensive study comparing TFMs, GBDTs, and classical baselines on the 112 datasets of the TALENT benchmark. Our results reveal a performance-uncertainty trade-off: although TFMs achieve the highest predictive performance, measured by AUC, they exhibit lower conditional coverage under conformal prediction, measured by SSCS, compared to GBDTs. Complementary experiments on synthetic datasets further characterize the regimes in which this effect intensifies. We conclude that while TFMs advance predictive frontiers, achieving well-calibrated uncertainty remains a major open challenge for their reliable adoption. Code is available at: https://github.com/jose-melo/high-performance-low-reliability
Towards Benchmarking Foundation Models for Tabular Data With Text
Department of Computer Science, University of Freiburg, Freiburg, Germany（弗赖堡大学计算机科学系）；Prior Labs, Freiburg, Germany（Prior实验室）；ELLIS Institute, Tubingen, Germany（图宾根ELLIS研究所）
STRABLE: Benchmarking Tabular Machine Learning with Strings
SODA Team, INRIA Saclay（SODA团队，INRIA萨克莱实验室）；Probabl；University of Freiburg（弗赖堡大学）；Prior Labs；ELLIS Institute Tübingen（图宾根ELLIS研究所）；Technion – Israel Institute of Technology（技术ion-以色列理工学院）
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Technion – Israel Institute of Technology（技术ion – 以色列理工学院）；Prior Labs（Prior实验室）；NVIDIA；SODA Team, INRIA Saclay, Palaiseau（SODA团队，INRIA萨克莱，帕莱索）；University of Freiburg（弗赖堡大学）；Probabl；ELLIS Institute Tübingen（图宾根ELLIS研究所）
Tabular foundation model for GEOAI benchmark problems BM/AirportSoilProperties/2/2025
Department of Civil and Environmental Engineering, Tohoku University（土屋大学土木环境工程系）；Research Organization of Information and Systems, The Institute of Statistical Mathematics（信息与系统研究所，统计数学研究所）；The Graduate University for Advanced Studies, Department of Statistical Science（高级研究大学，统计科学系）
Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs
Department of Intensive Care and Neonatology and Children’s Research Center University of Zurich University Children’s Hospital Zurich（重症护理与新生儿科及儿童研究中心，苏黎世大学苏黎世儿童医院）；Department of Health Sciences and Technology ETH Zurich（健康科学与技术系，苏黎世联邦理工学院）；Department of Computer Science ETH Zurich（计算机科学系，苏黎世联邦理工学院）
Benchmarking Tabular Foundation Models for Conditional Density Estimation in Regression
Department of Statistics, Federal University of São Carlos (UFSCar)（圣卡洛斯联邦大学统计系）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔国立理工学院、让·库尔坦数学实验室）
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）
TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy
Renmin University of China（中国人民大学）；Cerebras Systems, Inc.（赛博拉斯系统公司）；National University of Singapore（新加坡国立大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
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）
TFMPathy: Tabular Foundation Model for Privacy-Aware, Generalisable Empathy Detection from Videos
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University（Curtin大学电气工程、计算与数学科学学院）；The Australian National University（澳大利亚国立大学）；North South University（北南大学）；University of ÓBuda（奥布达大学）
TabFORGE: A Tabular Foundation Model for Generative Modelling
Department of Computer Science and Technology, University of Cambridge, UK（计算机科学与技术系，剑桥大学，英国）；SAP SE；Télécom Paris, Institut Polytechnique de Paris, France（巴黎telecom，巴黎理工 institute，法国）
Explaining Tabular Foundation Model Differences Through Meta-Features
Clausthal University of Technology, Clausthal-Zellerfeld, Germany（Clausthal技术大学，Clausthal-Zellerfeld，德国）；University of Mannheim, Mannheim, Germany（曼海姆大学，曼海姆，德国）
GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Swiss Institute of Bioinformatics（瑞士生物信息学研究所）；Université Paris Cité（巴黎西岱大学）
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Google Research（谷歌研究院）；University of Southern California（南加州大学）；Google DeepMind（谷歌DeepMind）；Harvard University（哈佛大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
