Weihao Kong

TabFM is a 400M-parameter tabular foundation model that formulates supervised tabular prediction as in-context learning, is pretrained entirely on synthetic tables generated from structural causal models, and produces calibrated zero-shot predictions in a single forward pass; across all 51 TabArena datasets (38 classification, 13 regression) zero-shot TabFM ranks first among default tabular foundation models and outperforms tuned AutoML pipelines, while its two extensions, TabFM+ (multi-view feature expansion, ensembling, and post-hoc calibration) and TabFM-Auto (LLM-guided, dataset-specific data processing and feature engineering), improve both tracks over the same frozen weights.

TabFM is a 400M-parameter tabular foundation model that formulates supervised tabular prediction as in-context learning, is pretrained entirely on synthetic tables generated from structural causal models, and produces calibrated zero-shot predictions in a single forward pass; across all 51 TabArena datasets (38 classification, 13 regression) zero-shot TabFM ranks first among default tabular foundation models and outperforms tuned AutoML pipelines, while its two extensions, TabFM+ (multi-view feature expansion, ensembling, and post-hoc calibration) and TabFM-Auto (LLM-guided, dataset-specific data processing and feature engineering), improve both tracks over the same frozen weights.