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2002.01381
Cited By

On the Inference of Applying Gaussian Process Modeling to a
Deterministic Function

v1v2v3 (latest)

On the Inference of Applying Gaussian Process Modeling to a Deterministic Function

Electronic Journal of Statistics (EJS), 2020

4 February 2020

ArXiv (abs)PDF HTML

Papers citing "On the Inference of Applying Gaussian Process Modeling to a Deterministic Function"

9 / 9 papers shown

Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data

Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data

Ronald Pandolfi

421

1

0

07 Nov 2024

Asymptotic Theory for Linear Functionals of Kernel Ridge Regression

Asymptotic Theory for Linear Functionals of Kernel Ridge Regression

239

4

0

07 Mar 2024

Comparing Scale Parameter Estimators for Gaussian Process Interpolation
with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum
Likelihood

Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood

Masha Naslidnyk

Motonobu Kanagawa

Maren Mahsereci

321

1

0

14 Jul 2023

Random Smoothing Regularization in Kernel Gradient Descent Learning

Random Smoothing Regularization in Kernel Gradient Descent Learning

Liang Ding

Tianyang Hu

273

8

0

05 May 2023

Maximum Likelihood Estimation in Gaussian Process Regression is
Ill-Posed

Maximum Likelihood Estimation in Gaussian Process Regression is Ill-PosedJournal of machine learning research (JMLR), 2022

376

37

0

17 Mar 2022

Error analysis for a statistical finite element method

Error analysis for a statistical finite element methodJournal of Multivariate Analysis (J. Multivar. Anal.), 2022

176

6

0

19 Jan 2022

Smooth Nested Simulation: Bridging Cubic and Square Root Convergence
Rates in High Dimensions

Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High DimensionsManagement Sciences (MS), 2022

473

8

0

09 Jan 2022

Convergence of Gaussian process regression: Optimality, robustness, and
relationship with kernel ridge regression

Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression

368

5

0

20 Apr 2021

Maximum likelihood estimation and uncertainty quantification for
Gaussian process approximation of deterministic functions

Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions

Simo Särkkä

348

45

0

29 Jan 2020

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