activity
20082025
most citedAdditive Kernels for Gaussian Process Modeling

13 citations · 40 across the 25 of their papers we have counts for

collaborators

34 papers

stat.ME2025

Estimation and model errors in Gaussian-process-based Sensitivity Analysis of functional outputs

Yuri Taglieri Sáo, Olivier Roustant, Geraldo de Freitas Maciel

Global sensitivity analysis (GSA) of functional-output models is usually performed by combining statistical techniques, such as basis expansions, metamodeling and sampling based es…

math.OC2025

A methodology for creating multidisciplinary design optimization benchmark problems from optimization ones

Matthias De Lozzo, Olivier Roustant, Amine Aziz-Alaoui

Benchmark problems with known solutions play a central role in the assessment of optimization algorithms. While mono-disciplinary optimization benefits from a rich collection of su…

stat.AP2025

Simulation of extreme functionals in meteoceanic data: Application to surge evolution over tidal cycles

Nathan Gorse, Olivier Roustant, Jérémy Rohmer +1

We investigate the influence of time-varying meteoceanic conditions on coastal flooding under the prism of rare events. Focusing on conditions observed over half tidal cycles, we o…

math.ST2025

Non-asymptotic confidence regions on RKHS. The Paley-Wiener and standard Sobolev space cases

Fabrice Gamboa, Olivier Roustant

We consider the problem of constructing a global, probabilistic, and non-asymptotic confidence region for an unknown function observed on a random design. The unknown function is a…

math.ST2025

General reproducing properties in RKHS with application to derivative and integral operators

Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier Roustant

In this paper, we consider the reproducing property in Reproducing Kernel Hilbert Spaces (RKHS). We establish a reproducing property for the closure of the class of combinations of…

math.ST2024

Fast pick-freeze estimation of Sobol' sensitivity maps using basis expansions

Yuri Sao, Olivier Roustant, Geraldo de Freitas Maciel

Global sensitivity analysis (GSA) aims at quantifying the contribution of input variables over the variability of model outputs. In the frame of functional outputs, a common goal i…