arXiv Machine Learning
Aug 14

TabH2O: A Unified Foundation Model for Tabular Prediction

arXiv:2605. 18383v2 Announce Type: replace Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning.

By Pascal Pfeiffer, Dmitry Gordeev, Mathias M\"uller, Laura Fink, Joan Salv\`a Soler, Mark Landry, Branden Murray, Marcos V. Conde, Sri Satish Ambati
arXiv AI
Sep 4

Xiaomi-TabLDM: A Tabular Foundation Model Technical Report

Xiaomi-TabLDM is a tabular foundation model that performs classification and regression via in-context learning without task‑specific fine‑tuning. It is pretrained solely on synthetic data from structural causal models, achieving top‑ranked regression results on multiple benchmarks while reducing training and prediction time compared to leading models. The architecture incorporates a three‑stage training strategy, dual‑stream feature grouping, lightweight attention residuals, and sparse mixture‑of‑experts, and it can further improve accuracy through test‑time compute scaling.

By TabLDM Team, Penghui Wang, Wei Liu, Hong Wang, Chengyue Huang, Yuxi Sun, Zirui Wang, Hongming Huang, Quan Wang, Chunxiao Liu, Erli Meng, Bin Wang
arXiv AI
Jun 30

Beyond IID: How General Are Tabular Foundation Models, Really?

arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.

By Lennart Purucker, Andrej Tschalzev, Nick Erickson, Gioia Blayer, David Holzm\"uller, Alan Arazi, Alexander Pfefferle, Mustafa Tajjar, Ga\"el Varoquaux, Frank Hutter