Abstract
This article investigates the origin of numerical issues in maximum likelihood parameter estimation for Gaussian process (GP) interpolation and investigates simple but effective strategies for improving commonly used open-source software implementations. This work targets a basic problem but a host of studies, particularly in the literature of Bayesian optimization, rely on off-the-shelf GP implementations. For the conclusions of these studies to be reliable and reproducible, robust GP implementations are critical.
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Notes
- 1.
Code available at https://github.com/saferGPMLE.
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Basak, S., Petit, S., Bect, J., Vazquez, E. (2022). Numerical Issues in Maximum Likelihood Parameter Estimation for Gaussian Process Interpolation. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13164. Springer, Cham. https://doi.org/10.1007/978-3-030-95470-3_9
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