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Towards new cross-validation-based estimators for Gaussian process
  regression: efficient adjoint computation of gradients
v1v2 (latest)

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

26 February 2020
S. Petit
Julien Bect
Sébastien Da Veiga
Paul Feliot
E. Vázquez
ArXiv (abs)PDFHTML

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

5 / 5 papers shown
Robust and Conjugate Gaussian Process Regression
Robust and Conjugate Gaussian Process RegressionInternational Conference on Machine Learning (ICML), 2023
Matias Altamirano
F. Briol
Jeremias Knoblauch
421
18
0
01 Nov 2023
Parameter selection in Gaussian process interpolation: an empirical
  study of selection criteria
Parameter selection in Gaussian process interpolation: an empirical study of selection criteria
S. Petit
Julien Bect
Paul Feliot
E. Vázquez
453
15
0
13 Jul 2021
Scalable Cross Validation Losses for Gaussian Process Models
Scalable Cross Validation Losses for Gaussian Process Models
M. Jankowiak
Geoff Pleiss
249
6
0
24 May 2021
Numerical issues in maximum likelihood parameter estimation for Gaussian
  process interpolation
Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolationInternational Conference on Machine Learning, Optimization, and Data Science (MOD), 2021
S. Basak
S. Petit
Julien Bect
E. Vázquez
226
17
0
24 Jan 2021
Fast calculation of Gaussian Process multiple-fold cross-validation
  residuals and their covariances
Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariancesJournal of Computational And Graphical Statistics (JCGS), 2021
D. Ginsbourger
Cedric Scharer
276
11
0
08 Jan 2021
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