Gaussian processes are a widely embraced technique for regression due to their good prediction accuracy, analytical tractability, and built-in capabilities for uncertainty quantification. However, they suffer from the curse of dimensionality whenever the number of variables increases. This challenge is generally addressed by assuming additional structure in the problem, the preferred options being either additivity or low intrinsic dimensionality. After discussing their relative merits, our contribution for high-dimensional Gaussian process modeling is to combine them with a multi-fidelity strategy. We detail the corresponding construction and showcase the advantages through experiments on synthetic functions and datasets.

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