arXiv:2606.01427v1 Announce Type: cross Abstract: 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
The rapid advancement and widespread adoption of foundation models necessitate a deeper understanding of their capabilities, especially concerning critical aspects like uncertainty quantification, given their deployment in increasingly sensitive applications.
Reliable uncertainty quantification in foundation models is crucial for their trustworthiness and applicability in domains requiring high assurance, influencing adoption rates and regulatory frameworks.
This research provides empirical evidence regarding the UQ abilities of tabular FMs, which could lead to improved model architectures or more confident deployment decisions in various industries.
- · AI developers
- · Industries requiring reliable predictions (e.g., finance, engineering)
- · Data scientists
- · AI models lacking robust UQ capabilities
Increased confidence in deploying foundation models in critical regression tasks requiring uncertainty estimates.
Development of improved or standardized UQ methodologies for a broader range of foundation models and data types.
Acceleration of AI adoption in highly regulated industries as UQ challenges are systematically addressed.
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Read at arXiv cs.LG