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. This paper presents MFTabPFN, a transformer-based multi-fidelity learning framework that integrates low- and high-fidelity data for improved prediction and uncertainty quantification. Across diverse benchmarks, it outperforms existing methods and supports efficient active learning for resource intensive applications.
Shi et al. (Sat,) studied this question.
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