Author

Rajat Sen

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#machine learning Preprint Sep 2026

TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models

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 auxiliary files that carry dataset semantics. Meanwhile, self-evolving machine learning engineering (MLE) agents train models from scratch on each d...

Deqing Fu, Huang-Yuan Su, Rajat Sen et al. · 1 citation

#machine learning Preprint Sep 2026

TabFM: A Zero-Shot Foundation Model for Tabular Data

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-parameter tabular foundation model that formulates supervised tabular prediction as in-context learning. TabFM produces calibrated zero-shot predict...

Wei-Hao Kong, Erez Louidor Ilan, Shu-Xin Nie et al. · 9 citations · ⚡2