16 citations · 23 across the 10 of their papers we have counts for
10 papers
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Raphaël Carpintero Perez, Sébastien da Veiga, Josselin Garnier +1
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solv…
Sensitivity analysis from a single input/output sample
Sébastien Da Veiga, Fabrice Gamboa, Thierry Klein +2
The main objective of this paper is to estimate optimally Sobol' indices at any order when a unique input/output i.i.d.\ sample is available. Our approach stands on three main ingr…
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Clément Bénard, Brian Staber, Sébastien Da Veiga
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize…
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
Brian Staber, Sébastien Da Veiga
Due to the growing adoption of deep neural networks in many fields of science and engineering, modeling and estimating their uncertainties has become of primary importance. Despite…
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Clément Bénard, Gérard Biau, Sébastien da Veiga +1
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects a…
A sampling criterion for constrained Bayesian optimization with uncertainties
Reda El Amri, Rodolphe Le Riche, Céline Helbert +2
We consider the problem of chance constrained optimization where it is sought to optimize a function and satisfy constraints, both of which are affected by uncertainties. The real…
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
Clément Bénard, Sébastien da Veiga, Erwan Scornet
Variable importance measures are the main tools to analyze the black-box mechanisms of random forests. Although the mean decrease accuracy (MDA) is widely accepted as the most effi…
Global Sensitivity Analysis with Dependence Measures
Sébastien Da Veiga
Global sensitivity analysis with variance-based measures suffers from several theoretical and practical limitations, since they focus only on the variance of the output and handle…
Efficient Estimation of Sensitivity Indices
Sébastien Da Veiga, Fabrice Gamboa
In this paper we address the problem of efficient estimation of Sobol sensitivy indices. First, we focus on general functional integrals of conditional moments of the form $\E(ψ(\E…
Efficient estimation of conditional covariance matrices for dimension reduction
Sébastien Da Veiga, Jean-Michel Loubes, Maikol Solís
Let and . In this paper we propose an estimator of the conditional covariance matrix, $\mathrm{Cov}(\mathbb{E}[\boldsymbol{X}\vert…