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Practical heteroskedastic Gaussian process modeling for large simulation experiments

Practical heteroskedastic Gaussian process modeling for large simulation experiments

M. Binois
R. Gramacy
M. Ludkovski
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Papers citing "Practical heteroskedastic Gaussian process modeling for large simulation experiments"

14 / 14 papers shown
Title
Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functions
Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functions
Mickael Binois
Jeffrey Larson
74
0
0
29 Apr 2025
Multi-Response Heteroscedastic Gaussian Process Models and Their Inference
Multi-Response Heteroscedastic Gaussian Process Models and Their Inference
Taehee Lee
Jun S. Liu
11
1
0
29 Aug 2023
Active Learning of Piecewise Gaussian Process Surrogates
Active Learning of Piecewise Gaussian Process Surrogates
Chiwoo Park
R. Waelder
Bonggwon Kang
Benji Maruyama
Soondo Hong
R. Gramacy
GP
19
1
0
20 Jan 2023
A review of predictive uncertainty estimation with machine learning
A review of predictive uncertainty estimation with machine learning
Hristos Tyralis
Georgia Papacharalampous
UD
UQCV
53
43
0
17 Sep 2022
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method
Bruno Barracosa
Julien Bect
H. Baraffe
J. Morin
Josselin Fournel
E. Vázquez
32
2
0
08 Jul 2022
Stochastic polynomial chaos expansions to emulate stochastic simulators
Stochastic polynomial chaos expansions to emulate stochastic simulators
X. Zhu
Bruno Sudret
15
17
0
07 Feb 2022
Scaling Bayesian Optimization With Game Theory
Scaling Bayesian Optimization With Game Theory
L. Mathesen
G. Pedrielli
R. L. Smith
16
1
0
07 Oct 2021
mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms
mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms
M. Ludkovski
28
7
0
01 Dec 2020
Energy consumption forecasting using a stacked nonparametric Bayesian approach
Energy consumption forecasting using a stacked nonparametric Bayesian approach
Dilusha Weeraddana
N. Khoa
Lachlan OÑeil
Weihong Wang
C. Cai
AI4TS
8
3
0
11 Nov 2020
KrigHedge: Gaussian Process Surrogates for Delta Hedging
KrigHedge: Gaussian Process Surrogates for Delta Hedging
M. Ludkovski
Yuri F. Saporito
35
5
0
16 Oct 2020
Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models
Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models
Xujia Zhu
Bruno Sudret
16
37
0
04 May 2020
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Ryan-Rhys Griffiths
Alexander A. Aldrick
Miguel García-Ortegón
Vidhi R. Lalchand
A. Lee
31
35
0
17 Oct 2019
The Kalai-Smorodinski solution for many-objective Bayesian optimization
The Kalai-Smorodinski solution for many-objective Bayesian optimization
M. Binois
Victor Picheny
P. Taillandier
A. Habbal
24
17
0
18 Feb 2019
Local Gaussian process approximation for large computer experiments
Local Gaussian process approximation for large computer experiments
R. Gramacy
D. Apley
127
391
0
02 Mar 2013