Link prediction is a fundamental task in graph machine learning with widespread applications such as recommendation systems, drug discovery, knowledge graphs, etc. In the foundation model era, how to develop universal link prediction methods across datasets and domains becomes a key problem, with some initial attempts adopting Graph Foundation Models utilizing Graph Neural Networks and Large Language Models. However, the existing methods face notable limitations, including limited pre-training scale or heavy reliance on textual information. Motivated by the success of tabular foundation models (TFMs) in achieving universal prediction across diverse tabular datasets, we explore an alternative approach by TFMs, which are pre-trained on diverse synthetic datasets sampled from structural causal models and support strong in-context learning independent of textual attributes. Nevertheless, adapting TFMs for link prediction faces severe technical challenges such as how to obtain the necessary context and capture link-centric topological information. To solve these challenges, we propose TFMLinker (Tabular Foundation Model for Link Predictor), aiming to leverage the in-context learning capabilities of TFMs to perform link prediction across diverse graphs without requiring dataset-specific fine-tuning. Specifically, we first develop a prototype-augmented local-global context module to construct context that captures both graph-specific and cross-graph transferable patterns. Next, we design a universal topology-aware link encoder to capture link-centric topological information and generate link representations as inputs for the TFM. Finally, we employ the TFM to predict link existence through in-context learning. Experiments on 6 graph benchmarks across diverse domains demonstrate the superiority of our method over state-of-the-art baselines without requiring dataset-specific finetuning.
MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement
GE Healthcare（GE医疗）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carle Illinois College of Medicine, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校卡尔伊利诺伊医学院）
FlagGAM: Rule-Basis Generalized Additive Models for Explainable Tabular Prediction
EECS Department, Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院电子工程与计算机科学系）；Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院斯隆管理学院）
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）
GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data
University of Cambridge（剑桥大学）；Precision Breast Cancer Institute（精准乳腺癌研究所）；Department of Computer Science and Technology（计算机科学与技术系）；Department of Oncology（肿瘤学系）
Understanding the Surprising Generalization Properties of Tabular Foundation Models
Polytechnique Montréal（蒙特利尔理工学院）；Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）；Chandar Research Lab（钱达尔研究实验室）；University of Toronto（多伦多大学）；Layer 6 AI（第六层人工智能公司）；Prior Labs（普里奥实验室）；ELLIS Institute Tübingen（埃利斯研究所图宾根分部）；University of Freiburg（弗赖堡大学）；Cohere（科here公司）
Explainability in mulimodal deep transformation models for stroke outcome prediction
University Hospital Zurich, Department of Neurology（苏黎世大学医院神经内科）；Zurich University of Applied Sciences, Institute of Data Analysis and Process Design (IDP)（苏黎世应用科学大学数据分析与流程设计研究所）；University of Zurich, Epidemiology, Biostatistics and Prevention Institute (EBPI)（苏黎世大学流行病学、生物统计学与预防研究所）；Thurgau Institute for Digital Transformation (TIDIT)（图尔高数字转型研究所）
Do causal predictors generalize better to new domains?
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（蒂宾根人工智能中心）；Max Planck ETH Center for Learning Systems（马克斯·普朗克ETH学习系统中心）
Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift
Department of Mathematics, Rhodes University, South Africa（数学系，罗德斯大学，南非）；National Institute for Theoretical and computational Sciences (NITheCS), Stellenbosch, 7600, South Africa（理论与计算科学国家研究所（NITheCS），斯泰伦博斯，7600，南非）；Interdisciplinary Research Program in Public Health, University of Burundi, Burundi（公共卫生跨学科研究计划，布恩迪大学，布恩迪）；Universite Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, Clermont–Ferrand, France（克莱蒙特-奥弗涅大学，克莱蒙特-奥弗涅INP，CNRS，帕西尔研究所，克莱蒙特-费尔南，法国）；Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, UK（国际公共卫生系，利物浦热带医学学校，利物浦，英国）；College of Health Solutions, Arizona State University, Phoenix, USA（健康解决方案学院，亚利桑那州立大学，凤凰城，美国）；Department of Statistics, University of Pretoria, Pretoria, South Africa（统计系，普里特oria大学，普里特oria，南非）
DiNo and RanBu: Lightweight Predictions from Shallow Random Forests
Insper Institute of Education and Research（Insper教育与研究学院）；Department of Statistics（统计系）；Federal University of São Carlos（圣卡洛斯联邦大学）；Institute of Mathematics and Statistics（数学与统计学研究所）；University of São Paulo（圣保罗大学）
Division of Natural and Applied Sciences, Duke Kunshan University, China（杜克-昆山大学自然科学与应用科学系）；School of Cyber Science and Engineering, Sichuan University, China（四川大学网络科学与工程学院）；Boston University, USA（波士顿大学）
