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

arXiv: https://arxiv.org/abs/2002.11543 · HTML: https://mlsanity.com/paper/2002.11543/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Robust and Conjugate Gaussian Process Regression](https://mlsanity.com/paper/2311.00463/) (2023)
- [Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation](https://mlsanity.com/paper/2101.09747/) (2021)
- [Scalable Cross Validation Losses for Gaussian Process Models](https://mlsanity.com/paper/2105.11535/) (2021)
- [Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances](https://mlsanity.com/paper/2101.03108/) (2021)
- [Parameter selection in Gaussian process interpolation: an empirical study of selection criteria](https://mlsanity.com/paper/2107.06006/) (2021)

## Abstract (arXiv)

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.