Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients

Sébastien Petit; Julien Bect; Sébastien da Veiga; Paul Feliot; Emmanuel Vazquez

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arXiv · 2002.11543

Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients

Sébastien Petit·Julien Bect·Sébastien da Veiga·Paul Feliot·Emmanuel Vazquez

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Abstract

We consider the problem of estimating the parameters of the covariance function of a Gaussian process by cross-validation. We suggest using new cross-validation criteria derived from the literature of scoring rules. We also provide an efficient method for computing the gradient of a cross-validation criterion. To the best of our knowledge, our method is more efficient than what has been proposed in the literature so far. It makes it possible to lower the complexity of jointly evaluating leave-one-out criteria and their gradients.

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Sébastien Petit, Julien Bect, Sébastien da Veiga, Paul Feliot, Emmanuel Vazquez. 2020-02-26. Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients. https://arxiv.org/abs/2002.11543

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