45 citations · 93 across the 6 of their papers we have counts for
6 papers
Kernel-based ANOVA decomposition and Shapley effects -- Application to global sensitivity analysis
Sébastien da Veiga
Global sensitivity analysis is the main quantitative technique for identifying the most influential input variables in a numerical simulation model. In particular when the inputs a…
Interpretable Random Forests via Rule Extraction
Clément Bénard, Gérard Biau, Sébastien da Veiga +1
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules. State-of-the-art l…
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients
Sébastien Petit, Julien Bect, Sébastien da Veiga +2
We consider the problem of estimating the parameters of the covariance function of a Gaussian process by cross-validation. We suggest using new cross-validation criteria derived fr…
Global sensitivity analysis for optimization with variable selection
Adrien Spagnol, Rodolphe Le Riche, Sebastien Da Veiga
The optimization of high dimensional functions is a key issue in engineering problems but it frequently comes at a cost that is not acceptable since it usually involves a complex a…
Local Polynomial Estimation for Sensitivity Analysis on Models With Correlated Inputs
Sébastien Da Veiga, François Wahl, Fabrice Gamboa
Sensitivity indices when the inputs of a model are not independent are estimated by local polynomial techniques. Two original estimators based on local polynomial smoothers are pro…
Global Sensitivity Analysis of Stochastic Computer Models with joint metamodels
Bertrand Iooss, Mathieu Ribatet, Amandine Marrel
The global sensitivity analysis method, used to quantify the influence of uncertain input variables on the response variability of a numerical model, is applicable to deterministic…