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cs.LG2026
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Deqing Fu, Huangyuan Su, Rajat Sen +4
Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, task descriptions, and auxiliar…
cs.LG2026
TabFM: A Zero-Shot Foundation Model for Tabular Data
Weihao Kong, Erez Louidor Ilan, Shuxin Nie +6
Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We present TabFM, a 400M-paramete…