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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
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
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; 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
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
