David Ginsbourger
Cited by
Cited by
DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization
O Roustant, D Ginsbourger, Y Deville
Journal of statistical software 51, 1-55, 2012
Kriging is well-suited to parallelize optimization
D Ginsbourger, R Le Riche, L Carraro
Computational intelligence in expensive optimization problems, 131-162, 2010
A benchmark of kriging-based infill criteria for noisy optimization
V Picheny, T Wagner, D Ginsbourger
Structural and multidisciplinary optimization 48, 607-626, 2013
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
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 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
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
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
On uncertainty quantification in hydrogeology and hydrogeophysics
N Linde, D Ginsbourger, J Irving, F Nobile, A Doucet
Advances in Water Resources 110, 166-181, 2017
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
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
A supermartingale approach to Gaussian process based sequential design of experiments
J Bect, F Bachoc, D Ginsbourger
Bernoulli 25 (4A), 2883-2919, 2019
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
Dealing with asynchronicity in parallel Gaussian process based global optimization
D Ginsbourger, J Janusevskis, R Le Riche
Technical Report, 2010
Towards Gaussian process-based optimization with finite time horizon
D Ginsbourger, R Le Riche
mODa 9–Advances in Model-Oriented Design and Analysis: Proceedings of the …, 2010
Stochastic versus deterministic approaches
P Renard, A Alcolea, D Ginsbourger
Environmental Modelling: Finding Simplicity in Complexity, Second Edition …, 2013
On the choice of the low-dimensional domain for global optimization via random embeddings
M Binois, D Ginsbourger, O Roustant
Journal of Global Optimization 76, 69-90, 2020
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
Multiples métamodèles pour l'approximation et l'optimisation de fonctions numériques multivariables
D Ginsbourger
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2009
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