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Ziegel, Johanna; Ginsbourger, David; Dümbgen, Lutz (2024). Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures. Bernoulli, 30(2), pp. 1441-1457. International Statistical Institute 10.3150/23-BEJ1639
Wyss, Patric; Ginsbourger, David; Shou, Haochang; Davatzikos, Christos; Klöppel, Stefan; Abdulkadir, Ahmed (2023). Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia. Scientific Reports, 13(1), p. 6406. Nature Publishing Group 10.1038/s41598-023-32867-z
Friedli, Lea; Linde, Niklas; Ginsbourger, David; Visentini, Alejandro Fernandez; Doucet, Arnaud (2023). Inference of geostatistical hyperparameters with the correlated pseudo-marginal method. Advances in water ressources, 173, p. 104402. Elsevier 10.1016/j.advwatres.2023.104402
Travelletti, Cédric; Ginsbourger, David; Linde, Niklas (2023). Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors. SIAM/ASA Journal on Uncertainty Quantification, 11(1), pp. 168-198. Society for Industrial and Applied Mathematics 10.1137/21M1445028
Allen, Sam; Ginsbourger, David; Ziegel, Johanna (2023). Evaluating Forecasts for High-Impact Events Using Transformed Kernel Scores. SIAM/ASA Journal on Uncertainty Quantification, 11(3), pp. 906-940. Society for Industrial and Applied Mathematics 10.1137/22M1532184
Fossum, Trygve Olav; Travelletti, Cédric; Eidsvik, Jo; Ginsbourger, David; Rajan, Kanna (2021). Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling. The annals of applied statistics, 15(2), pp. 597-618. Institute of Mathematical Statistics 10.1214/21-AOAS1451
Azzimonti, Dario; Ginsbourger, David; Chevalier, Clément; Bect, Julien; Richet, Yann (2021). Adaptive Design of Experiments for Conservative Estimation of Excursion Sets. Technometrics, 63(1), pp. 13-26. Taylor & Francis 10.1080/00401706.2019.1693427
Chevalier, Clément; Martius, Olivia; Ginsbourger, David (2021). Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling. Journal of computational and graphical statistics : JCGS, 30(3), pp. 745-755. American Statistical Association 10.1080/10618600.2020.1844213
Friedli, Lea; Ginsbourger, David; Bhend, Jonas (2021). Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions. Stochastic environmental research and risk assessment, 35(2), pp. 215-230. Springer 10.1007/s00477-020-01928-4
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
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
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
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
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
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
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
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
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
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
Maalouf, Eliane; Ginsbourger, David; Linde, Niklas (2022). Fast ABC with Joint Generative Modelling and Subset Simulation. In: Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science: Vol. 13163 (pp. 413-429). Cham: Springer International Publishing 10.1007/978-3-030-95467-3_30
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
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; 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
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
Gautier, Athénaïs; Ginsbourger, David; Pirot, Guillaume (August 2021). Goal-oriented adaptive sampling under random field modelling of response probability distributions. ESAIM: Proceedings and Surveys, 71, pp. 89-100. EDP Sciences 10.1051/proc/202171108
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
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
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
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.
