Ginsbourger, David

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Chevalier, Clément; Martius, Olivia; Ginsbourger, David (2020). Modeling non-stationary extreme dependence with stationary max-stable processes and multidimensional scaling. Journal of computational and graphical statistics : JCGS, pp. 1-11. American Statistical Association 10.1080/10618600.2020.1844213

Binois, Mickaël; Ginsbourger, David; Roustant, Olivier (2020). On the choice of the low-dimensional domain for high-dimensional bayesian optimization using random embeddings. Journal of global optimization, 76(1), pp. 69-90. Springer 10.1007/s10898-019-00839-1

Jaquier, Noémie; Ginsbourger, David; Calinon, Sylvain (2020). Learning from demonstration with model-based Gaussian process. In: Kaelbling, Leslie Pack; Kragic, Danica; Sugiura, Komei (eds.) Conference on Robot Learning. Proceedings of Machine Learning Research: Vol. 100 (pp. 247-257). PMLR

Buathong, Poompol; Ginsbourger, David; Krityakierne, Tipaluck (2020). Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization. In: Chiappa, Silvia; Calandra, Roberto (eds.) Twenty Third International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research: Vol. 108 (pp. 2731-2741). Online: PMLR

Bachoc, François; Suvorikova, Alexandra; Ginsbourger, David; Loubes, Jean-Michel; Spokoiny, Vladimir (2020). Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding. Electronic journal of statistics, 14(2), pp. 2742-2772. Institute of Mathematical Statistics 10.1214/20-EJS1725


Azzimonti, Dario; Ginsbourger, David; Rohmer, Jérémy; Idier, Déborah (2019). Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding. Technometrics, 61(4), pp. 474-493. Taylor & Francis 10.1080/00401706.2018.1562987

Pirot, Guillaume; Krityakierne, Tipaluck; Ginsbourger, David; Renard, Philippe (2019). Contaminant source localization via Bayesian global optimization. Hydrology and earth system sciences, 23(1), pp. 351-369. European Geosciences Union EGU 10.5194/hess-23-351-2019

Azzimonti, Dario; Ginsbourger, David; Chevalier, Clément; Bect, Julien; Richet, Yann (2019). Adaptive Design of Experiments for Conservative Estimation of Excursion Sets. Technometrics, 63(1), pp. 13-26. Taylor & Francis 10.1080/00401706.2019.1693427

Bect, Julien; Bachoc, François; Ginsbourger, David (2019). A supermartingale approach to Gaussian process based sequential design of experiments. Bernoulli, 25(4A), pp. 2883-2919. International Statistical Institute 10.3150/18-BEJ1074

Armentano, D.; Azaïs, J-M.; Ginsbourger, D; León, J.R. (2019). Conditions for the finiteness of the moments of the volume of level sets. Electronic communications in probability, 24(none) Institute of Mathematical Statistics 10.1214/19-ECP214


Madonna, Erica; Ginsbourger, David; Martius, Olivia (2018). A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables. Atmospheric research, 203, pp. 261-274. Elsevier 10.1016/j.atmosres.2017.11.024

Azzimonti, Dario Filippo; Ginsbourger, David (2018). Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation. Journal of computational and graphical statistics : JCGS, 27(2), pp. 255-267. American Statistical Association 10.1080/10618600.2017.1360781

Marmin, Sébastien Guillaume; Ginsbourger, David; Baccou, Jean; Liandrat, Jacques (2018). Warped Gaussian Processes and Derivative-Based Sequential Designs for Functions with Heterogeneous Variations. SIAM/ASA Journal on Uncertainty Quantification, 6(3), pp. 991-1018. Society for Industrial and Applied Mathematics 10.1137/17M1129179


Linde, Niklas; Ginsbourger, David; Irving, James; Nobile, Fabio; Doucet, Arnaud (2017). On uncertainty quantification in hydrogeology and hydrogeophysics. Advances in water ressources, 110, pp. 166-181. Elsevier 10.1016/j.advwatres.2017.10.014

Marmin, Sébastien Guillaume; Baccou, Jean; Liandrat, Jacques; Ginsbourger, David (2017). Non-parametric warping via local scale estimation for non-stationary Gaussian process modelling. In: Wavelets and Sparsity XVII (p. 72). SPIE 10.1117/12.2272408


Azzimonti, Dario Filippo; Bect, Julien; Chevalier, Clément; Ginsbourger, David (2016). Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior. SIAM/ASA Journal on Uncertainty Quantification, 4(1), pp. 850-874. Society for Industrial and Applied Mathematics 10.1137/141000749

Ginsbourger, David; Roustant, Olivier; Durrande, Nicolas (2016). On degeneracy and invariances of random fields paths with applications in Gaussian process modelling. Journal of statistical planning and inference, 170, pp. 117-128. Elsevier 10.1016/j.jspi.2015.10.002

Ginsbourger, David; Roustant, Olivier; Schuhmacher, Dominic; Durrande, Nicolas; Lenz, Nicolas (2016). On ANOVA Decompositions of Kernels and Gaussian Random Field Paths. In: Cools, Ronald; Nuyens, Dirk (eds.) Monte Carlo and Quasi-Monte Carlo Methods. Springer Proceedings in Mathematics & Statistics: Vol. 163 (pp. 315-330). Cham: Springer International Publishing 10.1007/978-3-319-33507-0_15


Josset, L.; Ginsbourger, David; Lunati, I. (2015). Functional error modeling for uncertainty quantification in hydrogeology. Water resources research, 51(2), pp. 1050-1068. American Geophysical Union 10.1002/2014WR016028

Binois, M.; Ginsbourger, David; Roustant, O. (2015). Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. European journal of operational research, 243(2), pp. 386-394. Elsevier 10.1016/j.ejor.2014.07.032

Chevalier, Clément; Emery, Xavier; Ginsbourger, David (2015). Fast Update of Conditional Simulation Ensembles. Mathematical Geosciences, 47(7), pp. 771-789. Springer 10.1007/s11004-014-9573-7

Krityakierne, Tipaluck; Ginsbourger, David (2015). Global Optimization with Sparse and Local Gaussian Process Models. In: Pardalos, Panos; Pavone, Mario; Farinella, Giovanni Maria; Cutello, Vincenzo (eds.) Machine Learning, Optimization, and Big Data. Lecture Notes in Computer Science: Vol. 9432 (pp. 185-196). Cham: Springer 10.1007/978-3-319-27926-8_16

Marmin, Sébastien Guillaume; Chevalier, Clément; Ginsbourger, David (2015). Differentiating the Multipoint Expected Improvement for Optimal Batch Design. In: Pardalos, Panos; Pavone, Mario; Farinella, Giovanni Maria; Cutello, Vincenzo (eds.) Machine Learning, Optimization, and Big Data. Lecture Notes in Computer Science: Vol. 9432 (pp. 37-48). Cham: Springer 10.1007/978-3-319-27926-8_4


Chevalier, Clément; Picheny, Victor; Ginsbourger, David (2014). KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging. Computational statistics & data analysis, 71, pp. 1021-1034. Elsevier 10.1016/j.csda.2013.03.008

Picheny, Victor; Ginsbourger, David (2014). Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package. Computational Statistics & Data Analysis, 71, pp. 1035-1053. Elsevier 10.1016/j.csda.2013.03.018

Chevalier, Clément; Ginsbourger, David; Emery, Xavier (2014). Corrected kriging update formulae for batch-sequential data assimilation. In: Pardo-Igúzquiza, Eulogio; Guardioloa, Albert; Heredia, Javier; Durán, Juan José; Vargas-Guzmán, Jose Antonio (eds.) Mathematics of Planet Earth: Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences. Lecture Notes in Earth System Sciences (pp. 119-122). Berlin: Springer 10.1007/978-3-642-32408-6_29

Ginsbourger, David; Baccou, Jean; Chevalier, Clément; Perales, Frédéric; Garland, Nicolas; Monerie, Yann (2014). Bayesian Adaptive Reconstruction of Profile Optima and Optimizers. SIAM/ASA Journal on Uncertainty Quantification, 2(1), pp. 490-510. Society for Industrial and Applied Mathematics 10.1137/130949555

Chevalier, Clément; Bect, Julien; Ginsbourger, David; Vazquez, Emmanuel; Picheny, Victor; Richet, Yann (2014). Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set. Technometrics, 56(4), pp. 455-465. Taylor & Francis 10.1080/00401706.2013.860918


Picheny, Victor; Wagner, Tobias; Ginsbourger, David (2013). A benchmark of kriging-based infill criteria for noisy optimization. Structural and Multidisciplinary Optimization, 48(3), pp. 607-626. Springer 10.1007/s00158-013-0919-4

Picheny, Victor; Ginsbourger, David (2013). A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations. SIAM/ASA Journal on Uncertainty Quantification, 1(1), pp. 57-78. Society for Industrial and Applied Mathematics 10.1137/120882834

Durrande, Nicolas; Ginsbourger, David; Roustant, Olivier; Carraro, Laurent (2013). ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis. Journal of multivariate analysis, 115, pp. 57-67. Elsevier 10.1016/j.jmva.2012.08.016

Ginsbourger, David; Rosspopoff, Bastien; Pirot, Guillaume; Durrande, Nicolas; Renard, Philippe (2013). Distance-based kriging relying on proxy simulations for inverse conditioning. Advances in water ressources, 52, pp. 275-291. Elsevier 10.1016/j.advwatres.2012.11.019

Picheny, Victor; Ginsbourger, David; Richet, Yann; Caplin, Gregory (2013). Quantile-Based Optimization of Noisy Computer Experiments with Tunable Precision. Technometrics, 55(1), pp. 2-13. Taylor & Francis 10.1080/00401706.2012.707580

Chevalier, Clément; Ginsbourger, David; Bect, Julien; Molchanov, Ilya (2013). Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with Gaussian process models. In: Uciński, Dariusz; Atkinson, Anthony C; Patan, Maciej (eds.) mODa 10 - Advances in Model-Oriented Design and Analysis. Proceedings of the 10th International Workshop in Model-Oriented Design and Analysis Held in Łagów Lubuski, Poland, June 10–14, 2013. Contributions to Statistics (pp. 35-43). Springer 10.1007/978-3-319-00218-7_5

Ginsbourger, David; Durrande, Nicolas; Roustant, Oliver (2013). Kernels and Designs for Modelling Invariant Functions: From Group Invariance to Additivity. In: Ucinski, Dariusz; Atkinson, Anthony C; Patan, Maciej (eds.) mODa 10 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics (pp. 107-115). Berlin: Springer 10.1007/978-3-319-00218-7_13


Bect, J.; Ginsbourger, D.; Li, L.; Picheny, V.; Vazquez, E. (2012). Sequential design of computer experiments for the estimation of a probability of failure. Statistics and computing, 22(3), pp. 773-793. New York, N.Y.: Springer 10.1007/s11222-011-9241-4

Durrande, N.; Ginsbourger, D.; Roustant, O. (2012). Additive Covariance Kernels for High-Dimensional Gaussian Process Modeling. Annales de la Faculté des Sciences de Toulouse - mathématiques, 21(3), pp. 481-499. Toulouse (F): Université Paul Sabatier

Ginsbourger, David; Bay, Xavier; Roustant, Olivier; Carraro, Laurent (2012). Argumentwise invariant kernels for the approximation of invariant functions. Annales de la Faculté des Sciences de Toulouse - mathématiques, 21(3), pp. 501-527. Toulouse (F): Université Paul Sabatier

Mahlstein, I.; Martius, Olivia; Chevalier, C.; Ginsbourger, D. (2012). Changes in the odds of extreme events in the Atlantic basin depending on the position of the extratropical jet. Geophysical Research Letters, 39(22), L22805. Washington, D.C.: American Geophysical Union 10.1029/2012GL053993

Roustant, O.; Ginsbourger, D.; Deville, Y. (2012). DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization. Journal of statistical software, 51(1), pp. 1-55. Los Angeles, Calif.: UCLA Statistics

Preuss, Mike; Wagner, Tobias; Ginsbourger, David (2012). High-Dimensional Model-Based Optimization Based on Noisy Evaluations of Computer Games. In: Hamadi, Youssef; Schoenauer, Marc (eds.) Learning and Intelligent Optimization. 6th International Conference, LION 6, Paris, France, January 16-20, 2012. Lecture Notes in Computer Science: Vol. 7219 (pp. 145-159). Heidelberg: Springer Verlag 10.1007/978-3-642-34413-8_11

Janusevskis, Janis; Le Riche, Rodolphe; Ginsbourger, David; Girdziusas, Ramunas (2012). Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions: Potentials and Challenges. In: Hamadi, Youssef; Schoenauer, Marc (eds.) Learning and Intelligent Optimization. 6th International Conference, LION 6, Paris, France, January 16-20, 2012. Lecture Notes in Computer Science: Vol. 7219 (pp. 413-418). Heidelberg: Springer Verlag 10.1007/978-3-642-34413-8_37


Ginsbourger, David; Le Riche, Rodolphe (2010). Towards Gaussian Process-based Optimization with Finite Time Horizon. In: Giovagnoli, A.; Atkinson, A.C.; Torsney, B.; May, C. (eds.) Moda 9 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics (pp. 89-96). Heidelberg: Springer Verlag 10.1007/978-3-7908-2410-0_12

Ginsbourger, David; Le Riche, Rodolphe; Carraro, L. (2010). Kriging is well-suited to parallelize optimization. In: Tenne, Y.; Goh, C.K. (eds.) Computational Intelligence in Expensive Optimization Problems. Adaptation, Learning, and Optimization: Vol. 2 (pp. 131-162). Heidelberg: Springer Verlag 10.1007/978-3-642-10701-6_6

Picheny, Victor; Ginsbourger, David (2010). Noisy Expected Improvement and on-line computation time allocation for the optimization of simulators with tunable fidelity. In: 2nd International Conference on Engineering Optimization.

Picheny, Victor; Ginsbourger, David; Roustant, Olivier (2010). Adaptive Designs of Experiments for Accurate Approximation of Target Regions. Journal of mechanical design, 132(7), 071008. New York, N.Y.: American Society of Mechanical Engineers ASME 10.1115/1.4001873

Smarslok, Benjamin P.; Haftka, Raphael T.; Carraro, Laurent; Ginsbourger, David (2010). Improving accuracy of failure probability estimates with Separable Monte Carlo. International journal of reliability and safety, 4(4), pp. 393-414. Olney, UK: Inderscience Enterprises Limited 10.1504/IJRS.2010.035577

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