A multi-fidelity tabular prior-data fitted network model for accurate prediction and uncertainty quantification.

Abstract

Accurate prediction of unknown labels from feature-label datasets using machine learning is critical for applications spanning drug discovery, disease diagnostics, and climate science. However, challenges persist with limited data, high-dimensional inputs, and multi-fidelity scenarios. We developed multi-fidelity tabular prior-data fitted network (MFTabPFN), a general-purpose multi-fidelity model integrating low- and high-fidelity data through a hierarchical transformer architecture to enhance prediction accuracy and uncertainty quantification (UQ). MFTabPFN captures cross-fidelity correlations while seamlessly adapting to single-fidelity data. An active learning framework further enhances scalability by prioritizing high-value data for model refinement, minimizing resource demands in resource-intensive tasks. Evaluated on various tasks such as forest fire burned area prediction, wine quality assessment, and computational fluid dynamics, MFTabPFN outperforms state-of-the-art methods, achieving varying degrees of prediction accuracy improvement. Its versatility and robust prediction and UQ capabilities across single- and multi-fidelity datasets position MFTabPFN as a promising tool for data-driven discovery in diverse applications.