Research Papers

What this post added

Introduces TabH2O, a foundation model for tabular data, detailing its unified training approach with a dual-head architecture, single-stage pretraining with specific stability techniques, and noise-aware pretraining. Presents evaluation results on the TALENT benchmark, comparing its performance against CatBoost, H2O AutoML, LightGBM, TabPFN, and TabICL.