RelBench: Relational Deep Learning Benchmark
TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
The Relational Transformer architecture for Relational Foundation Models
🚀 Awesome TabPFN use-cases from around the world
(ICML 2026) GraphPFN: A Prior-Data Fitted Graph Foundation Model
Turning Tabular Foundation Models into Graph Foundation Models
A curated collection of research on tabular foundation models: models pretrained on large-scale (synthetic) data that generalize to unseen tabular tasks at inference time.
TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery
Tabular Foundation Models Adaptation for Survival Analysis
Discovering association rules from high dimensional small tabular data via neurosymbolic rule mining supported by fine-tuning using tabular foundation models
The official implementation for the paper, “Clustering Method for Tabular Data Based on Pretrained Foundation Models with Synthetic Data.”
[QDB'25] Out in the wild of modern data science, good tabular AI meets bad data. Who wins?
Can domain knowledge be encoded in a tabular foundation model's pretraining prior? TabICL prior design for credit-risk PD and LGD.
Hands-on materials for Day 3 of the ICGEB AI Workshop 2025, covering machine learning workflows for tabular data
Reproducible datasets, computational workflows, and model implementations for externally validated tabular foundation models and explainable concrete compressive strength prediction.
Exploration of tabular data foundation models
An engineering-first training platform and community for data and AI practitioners. Current focus: ML and tabular foundation models.
🚰 Active Tabular Augmentation via Policy-Guided Diffusion Inpainting [ICML 2026]
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