TabFM: A Zero-Shot Foundation Model for Tabular Data
TabFM, a 400M tabular foundation model, delivers calibrated zero-shot predictions that beat tuned AutoML.
TabFM is a 400-million-parameter foundation model that treats supervised tabular prediction as in-context learning and outputs calibrated zero-shot predictions in one forward pass. It is trained only on synthetic tables generated from structural causal models. Across all 51 TabArena datasets, 38 classification and 13 regression, zero-shot TabFM ranks first among default tabular foundation models and beats tuned AutoML pipelines. Frozen-weight extensions TabFM+ and TabFM-Auto further improve both tracks.
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