| 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 | 178 | 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 | 166 | 2014 |
| A global quantification of compound precipitation and wind extremes O Martius, S Pfahl, C Chevalier Geophysical Research Letters 43 (14), 7709-7717, 2016 | 165 | 2016 |
| 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 | 107 | 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 | 81 | 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 | 67 | 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 | 65 | 2015 |
| Fast uncertainty reduction strategies relying on Gaussian process models C Chevalier University of Bern, 2013 | 55 | 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 | 51* | 2014 |
| Corrected Kriging update formulae for batch-sequential data assimilation C Chevalier, D Ginsbourger Mathematics of Planet Earth, 119-122, 2014 | 50 | 2014 |
| 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 | 49 | 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 | 48 | 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 | 48 | 2012 |
| Adaptive Design of Experiments for Conservative Estimation of Excursion Sets D Azzimonti, D Ginsbourger, C Chevalier, J Bect, Y Richet | 46* | 2016 |
| 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 | 35 | 2014 |
| Fast Update of Conditional Simulation Ensembles C Chevalier, X Emery, D Ginsbourger Mathematical Geosciences 47 (7), 771--789, 2015 | 24 | 2015 |
| Some properties of nested Kriging predictors F Bachoc, N Durrande, D Rullière, C Chevalier arXiv preprint arXiv:1707.05708, 2017 | 14 | 2017 |
| Efficient batch-sequential bayesian optimization with moments of truncated gaussian vectors S Marmin, C Chevalier, D Ginsbourger arXiv preprint arXiv:1609.02700, 2016 | 12 | 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 | 9 | 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 | 5 | 2016 |
David GinsbourgerUniversity of BernVahvistettu sähköpostiosoite verkkotunnuksessa stat.unibe.ch
