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

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

26 February 2020

Sébastien Da Veiga

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

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Title
Robust and Conjugate Gaussian Process Regression Matias Altamirano F. Briol Jeremias Knoblauch 84 13 0 01 Nov 2023
Parameter selection in Gaussian process interpolation: an empirical study of selection criteria S. Petit Julien Bect Paul Feliot E. Vázquez 62 11 0 13 Jul 2021
Scalable Cross Validation Losses for Gaussian Process Models M. Jankowiak Geoff Pleiss 65 6 0 24 May 2021
Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation S. Basak S. Petit Julien Bect E. Vázquez 48 14 0 24 Jan 2021
Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances D. Ginsbourger Cedric Scharer 52 9 0 08 Jan 2021