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1503.00021
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Mercer kernels and integrated variance experimental design: connections between Gaussian process regression and polynomial approximation
27 February 2015
Alex A. Gorodetsky
Youssef M. Marzouk
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Papers citing
"Mercer kernels and integrated variance experimental design: connections between Gaussian process regression and polynomial approximation"
13 / 13 papers shown
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes
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Christoph Zimmer
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454
0
0
17 May 2024
Active Learning in CNNs via Expected Improvement Maximization
Udai G. Nagpal
David A. Knowles
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197
2
0
27 Nov 2020
Locally induced Gaussian processes for large-scale simulation experiments
Statistics and computing (Stat. Comput.), 2020
D. Cole
R. Christianson
R. Gramacy
321
25
0
28 Aug 2020
Sparse Gaussian Process Based On Hat Basis Functions
Wenqi Fang
Huiyun Li
Hui Huang
Shaobo Dang
Zhejun Huang
Zheng Wang
151
1
0
15 Jun 2020
Adaptive Gaussian process surrogates for Bayesian inference
Timur Takhtaganov
Juliane Müller
GP
TPM
243
11
0
27 Sep 2018
Bayesian quadrature and energy minimization for space-filling design
L. Pronzato
A. Zhigljavsky
327
9
0
31 Aug 2018
Semi-intrusive uncertainty propagation for multiscale models
A. Nikishova
Alfons G. Hoekstra
195
12
0
25 Jun 2018
Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models
Sanmitra Ghosh
D. Gavaghan
Gary R. Mirams
47
10
0
25 May 2018
Gradient-based Optimization for Regression in the Functional Tensor-Train Format
Alex A. Gorodetsky
J. Jakeman
332
38
0
03 Jan 2018
Inverse modeling of hydrologic systems with adaptive multi-fidelity Markov chain Monte Carlo simulations
Jiangjiang Zhang
J. Man
Guang Lin
Laosheng Wu
L. Zeng
242
45
0
06 Dec 2017
Replication or exploration? Sequential design for stochastic simulation experiments
M. Binois
Jiangeng Huang
R. Gramacy
M. Ludkovski
283
125
0
09 Oct 2017
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
Hongqiao Wang
Jinglai Li
GP
322
65
0
29 Mar 2017
Probabilistic Numerical Methods for Partial Differential Equations and Bayesian Inverse Problems
Jon Cockayne
Chris J. Oates
T. Sullivan
Mark Girolami
368
46
0
25 May 2016
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