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Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics
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Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics

J. Akian
L. Bonnet
H. Owhadi
Éric Savin
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Papers citing "Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics"

7 / 7 papers shown
Title
Solving and Learning Nonlinear PDEs with Gaussian Processes
Solving and Learning Nonlinear PDEs with Gaussian Processes
Yifan Chen
Bamdad Hosseini
H. Owhadi
Andrew M. Stuart
79
157
0
24 Mar 2021
Learning dynamical systems from data: a simple cross-validation perspective
Learning dynamical systems from data: a simple cross-validation perspective
B. Hamzi
H. Owhadi
69
41
0
09 Jul 2020
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation
Yifan Chen
H. Owhadi
Andrew M. Stuart
103
31
0
22 May 2020
Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows
Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows
G. Yoo
H. Owhadi
69
21
0
19 Feb 2020
Kernel Flows: from learning kernels from data into the abyss
Kernel Flows: from learning kernels from data into the abyss
H. Owhadi
G. Yoo
88
90
0
13 Aug 2018
Asymptotic normality and efficiency of two Sobol index estimators
Asymptotic normality and efficiency of two Sobol index estimators
Alexandre Janon
T. Klein
Agnès Lagnoux-Renaudie
M. Nodet
Clémentine Prieur
60
231
0
26 Mar 2013
Calculations of Sobol indices for the Gaussian process metamodel
Calculations of Sobol indices for the Gaussian process metamodel
A. Marrel
Bertrand Iooss
Béatrice Laurent
O. Roustant
170
338
0
07 Feb 2008