On the Uncertainty Quantification Ability of Tabular Foundation Models
Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning. However, many critical applications in mechanics and computational science require not only accurate predictions but also reliable uncertainty quantification (UQ). Herein we investigate the UQ capabilities of tabular FMs in regression tasks through a comprehensive empirical study comparing Tabular Prior-Data Fitted Networks (TabPFN) against Gaussian processes (GPs). We systematically evaluate these two methods across a host of regression problems with varying complexity, dataset sizes, and input dimensionalities. We use a default setting to build all the ...
| Authors | Tyler R. Johnson, Kian Ben-Jacob, Nima Negarandeh et al. |
|---|---|
| Primary category | stat.ML |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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