David Ginsbourger
Cited by
Cited by
Dicekriging, Diceoptim: Two R packages for the analysis of computer experiments by kriging-based metamodelling and optimization
O Roustant, D Ginsbourger, Y Deville
Journal of Statistical Software 51 (1), 54p, 2012
Kriging is well-suited to parallelize optimization
D Ginsbourger, R Le Riche, L Carraro
Computational intelligence in expensive optimization problems, 131-162, 2010
Sequential design of computer experiments for the estimation of a probability of failure
J Bect, D Ginsbourger, L Li, V Picheny, E Vazquez
Statistics and Computing 22 (3), 773–793, 2012
Adaptive designs of experiments for accurate approximation of a target region
V Picheny, D Ginsbourger, O Roustant, RT Haftka, NH Kim
Journal of Mechanical Design 132 (7), 2010
A benchmark of kriging-based infill criteria for noisy optimization
V Picheny, T Wagner, D Ginsbourger
Structural and Multidisciplinary Optimization 48 (3), 607-626, 2013
Quantile-Based Optimization of Noisy Computer Experiments with Tunable Precision
V Picheny, D Ginsbourger, Y Richet, G Caplin
Technometrics 55 (1), 2-13, 2013
Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set
C Chevalier, J Bect, D Ginsbourger, E Vazquez, V Picheny, Y Richet
Technometrics 56 (4), 455-465, 2014
Fast computation of the multi-points expected improvement with applications in batch selection
C Chevalier, D Ginsbourger
International Conference on Learning and Intelligent Optimization, 59-69, 2013
Additive covariance kernels for high-dimensional Gaussian process modeling
N Durrande, D Ginsbourger, O Roustant
Annales de la faculté des sciences de Toulouse Mathématiques 21 (3), 481-499, 2012
Expected improvements for the asynchronous parallel global optimization of expensive functions: Potentials and challenges
J Janusevskis, R Le Riche, D Ginsbourger, R Girdziusas
International Conference on Learning and Intelligent Optimization, 413-418, 2012
ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis
N Durrande, D Ginsbourger, O Roustant, L Carraro
Journal of Multivariate Analysis 115, 57-67, 2013
Multiples métamodèles pour l'approximation et l'optimisation de fonctions numériques multivariables
D Ginsbourger
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2009
Towards Gaussian process-based optimization with finite time horizon
D Ginsbourger, R Le Riche
mODa 9–Advances in Model-Oriented Design and Analysis, 89-96, 2010
Dealing with asynchronicity in parallel Gaussian process based global optimization
D Ginsbourger, J Janusevskis, R Le Riche
Technical Report, 2010
Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations
M Binois, D Ginsbourger, O Roustant
European journal of operational research 243 (2), 386-394, 2015
A supermartingale approach to Gaussian process based sequential design of experiments
J Bect, F Bachoc, D Ginsbourger
Bernoulli 25 (4A), 2883-2919, 2019
DiceKriging: Kriging methods for computer experiments
O Roustant, D Ginsbourger, Y Deville
R package version 1, 2010
Differentiating the multipoint expected improvement for optimal batch design
S Marmin, C Chevalier, D Ginsbourger
International Workshop on Machine Learning, Optimization and Big Data, 37-48, 2015
Noisy kriging-based optimization methods: a unified implementation within the DiceOptim package
V Picheny, D Ginsbourger
Computational Statistics & Data Analysis 71, 1035-1053, 2014
Noisy expected improvement and on-line computation time allocation for the optimization of simulators with tunable fidelity
V Picheny, D Ginsbourger, Y Richet
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