Comparing scale parameter estimators for Gaussian process regression: Cross validation and maximum Likelihood

Naslidnyk, Masha; Kanagawa, Motonobu; Karvonen, Toni; Mahsereci, Maren
Submitted to ArXiV, 14 July 2023

Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of quantifying the uncertainties of predicted function values. For the quantified uncertainties to be well-calibrated, however, the covariance kernel of the GP prior has to be carefully selected. In this paper, we theoretically compare two methods for choosing the kernel in GP regression: cross-validation and maximum likelihood estimation. Focusing on the scale-parameter estimation of a Brownian motion kernel in the noiseless setting, we prove that cross-validation can yield asymptotically well-calibrated credible intervals for a broader class of ground-truth functions than maximum likelihood estimation, suggesting an advantage of the former over the latter.


Data Science
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