Cited by
Cited by
Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection
C Chevalier, D Ginsbourger
Learning and Intelligent Optimization, Lecture Notes in Computer Science, 59--69, 2013
A global quantification of compound precipitation and wind extremes
O Martius, S Pfahl, C Chevalier
Geophysical Research Letters 43 (14), 7709-7717, 2016
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
Clustering of Regional-scale Extreme Precipitation Events in Southern Switzerland
Y Barton, P Giannakaki, H von Waldow, C Chevalier, S Pfahl, O Martius
Monthly Weather Review, 2015
Nested Kriging predictions for datasets with a large number of observations
D Rullière, N Durrande, F Bachoc, C Chevalier
Statistics and Computing 28, 849-867, 2018
Differentiating the multipoint Expected Improvement for optimal batch design
S Marmin, C Chevalier, D Ginsbourger
Machine Learning, Optimization, and Big Data 9432, 37 - 48, 2016
Flood triggering in Switzerland: the role of daily to monthly preceding precipitation
P Froidevaux, J Schwanbeck, Weingartner, C Chevalier, O Martius
Hydrology and Earth System Sciences 19 (9), 3903--3924, 2015
Fast uncertainty reduction strategies relying on Gaussian process models
C Chevalier
University of Bern, 2013
KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging
C Chevalier, V Picheny, D Ginsbourger
Computational Statistics & Data Analysis 71, 1021-1034, 2014
Corrected Kriging update formulae for batch-sequential data assimilation
C Chevalier, D Ginsbourger
Mathematics of Planet Earth, 119-122, 2014
Adaptive Design of Experiments for Conservative Estimation of Excursion Sets
D Azzimonti, D Ginsbourger, C Chevalier, J Bect, Y Richet
Quantifying uncertainties on excursion sets under a Gaussian random field prior
D Azzimonti, J Bect, C Chevalier, D Ginsbourger
Accepted to SIAM/ASA J. Uncertainty Quantification, 2016
Estimating and Quantifying Uncertainties on Level Sets Using the Vorob'ev Expectation and Deviation with Gaussian Process Models
C Chevalier, D Ginsbourger, J Bect, I Molchanov
mODa 10 – Advances in Model-Oriented Design and Analysis, 2013
Changes in the odds of extreme events in the Atlantic basin depending on the position of the extratropical jet
I Mahlstein, O Martius, C Chevalier, D Ginsbourger
Geophysical Research Letters 39 (22), 2012
Bayesian adaptive reconstruction of profile optima and optimizers
D Ginsbourger, J Baccou, C Chevalier, F Perales, N Garland, Y Monerie
SIAM/ASA J. Uncertainty Quantification 2, 490--510, 2014
Fast Update of Conditional Simulation Ensembles
C Chevalier, X Emery, D Ginsbourger
Mathematical Geosciences 47 (7), 771--789, 2015
Some properties of nested Kriging predictors
F Bachoc, N Durrande, D Rullière, C Chevalier
arXiv preprint arXiv:1707.05708, 2017
Efficient batch-sequential bayesian optimization with moments of truncated gaussian vectors
S Marmin, C Chevalier, D Ginsbourger
arXiv preprint arXiv:1609.02700, 2016
Modeling nonstationary extreme dependence with stationary max-stable processes and multidimensional scaling
C Chevalier, O Martius, D Ginsbourger
Journal of Computational and Graphical Statistics 30 (3), 745-755, 2021
Design of Computer Experiments Using Competing Distances Between Set-Valued Inputs
D Ginsbourger, J Baccou, C Chevalier, F Perales
mODa 11-Advances in Model-Oriented Design and Analysis, 123--131, 2016
The system can't perform the operation now. Try again later.
Articles 1–20