Quantile-constrained Wasserstein projections for robust interpretability
of numerical and machine learning models

Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models

23 September 2022

Marouane Il Idrissi

Nicolas Bousquet

Jean-Michel Loubes

Papers citing "Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models"

6 / 6 papers shown

Title
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review Sibo Cheng César Quilodrán-Casas Said Ouala A. Farchi Che Liu ... Weiping Ding Yike Guo A. Carrassi Marc Bocquet Rossella Arcucci AI4CE 32 124 0 18 Mar 2023
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis Thomas Fel Rémi Cadène Mathieu Chalvidal Matthieu Cord David Vigouroux Thomas Serre MLAU FAtt AAML 112 58 0 07 Nov 2021
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA Clément Bénard Sébastien Da Veiga Erwan Scornet 37 49 0 26 Feb 2021
Out-of-Distribution Generalization via Risk Extrapolation (REx) David M. Krueger Ethan Caballero J. Jacobsen Amy Zhang Jonathan Binas Dinghuai Zhang Rémi Le Priol Aaron Courville OOD 215 901 0 02 Mar 2020
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution Topi Paananen Juho Piironen Michael Riis Andersen Aki Vehtari 76 50 0 21 Dec 2017
Density modification based reliability sensitivity analysis Paul Lemaître Ekatarina Sergienko A. Arnaud Nicolas Bousquet Fabrice Gamboa Bertrand Iooss 54 61 0 03 Oct 2012