66 citations · 258 across the 53 of their papers we have counts for
28 papers · 1 filter
On the coalitional decomposition of parameters of interest
Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa +2
Understanding the behavior of a black-box model with probabilistic inputs can be based on the decomposition of a parameter of interest (e.g., its variance) into contributions attri…
Bayesian quadrature for with Poincaré inequality on a compact interval
Olivier Roustant, Nora Lüthen, Fabrice Gamboa
Motivated by uncertainty quantification of complex systems, we aim at finding quadrature formulas of the form where belongs to $…
Fairness seen as Global Sensitivity Analysis
Clément Bénesse, Fabrice Gamboa, Jean-Michel Loubes +1
Ensuring that a predictor is not biased against a sensible feature is the key of Fairness learning. Conversely, Global Sensitivity Analysis is used in numerous contexts to monitor…
Weighted lens depth: Some applications to supervised classification
Alejandro Cholaquidis, Ricardo Fraiman, Fabrice Gamboa +1
Starting with Tukey's pioneering work in the 1970's, the notion of depth in statistics has been widely extended especially in the last decade. These extensions include high dimensi…
Global Sensitivity Analysis: a new generation of mighty estimators based on rank statistics
Fabrice Gamboa, Pierre Gremaud, Thierry Klein +1
We propose a new statistical estimation framework for a large family of global sensitivity analysis methods. Our approach is based on rank statistics and uses an empirical correlat…
Sensitivity analysis in general metric spaces
Fabrice Gamboa, Thierry Klein, Agnès Lagnoux +1
In this paper, we introduce new indices adapted to outputs valued in general metric spaces. This new class of indices encompasses the classical ones; in particular, the so-called S…
Optimal Uncertainty Quantification on moment class using canonical moments
Jerome Stenger, Fabrice Gamboa, Merlin Keller +1
We gain robustness on the quantification of a risk measurement by accounting for all sources of uncertainties tainting the inputs of a computer code. We evaluate the maximum quanti…
Sensitivity indices for output on a Riemannian manifold
R. Fraiman, F. Gamboa, L. Moreno
In the context of computer code experiments, sensitivity analysis of a complicated input-output system is often performed by ranking the so-called Sobol indices. One reason of the…
Obtaining fairness using optimal transport theory
Eustasio del Barrio, Fabrice Gamboa, Paula Gordaliza +1
Statistical algorithms are usually helping in making decisions in many aspects of our lives. But, how do we know if these algorithms are biased and commit unfair discrimination of…
Connecting pairwise spheres by depth: DCOPS
Ricardo Fraiman, Fabrice Gamboa, Leonardo Moreno
We extend the classical notion of the spherical depth in \mathbb{R}^k, to the important setup of data on a Riemannian manifold. We show that this notion of depth satisfies a set of…
Approximate Optimal Designs for Multivariate Polynomial Regression
Yohann De Castro, Fabrice Gamboa, Didier Henrion +2
We introduce a new approach aiming at computing approximate optimal designs for multivariate polynomial regressions on compact (semi-algebraic) design spaces. We use the moment-sum…
D-optimal design for multivariate polynomial regression via the Christoffel function and semidefinite relaxations
Yohann De Castro, F Gamboa, D Henrion +2
We present a new approach to the design of D-optimal experiments with multivariate polynomial regressions on compact semi-algebraic design spaces. We apply the moment-sum-of-square…
Conditional quantile sequential estimation for stochastic codes
Tatiana Labopin-Richard, Fabrice Gamboa, Aurélien Garivier +1
We propose and analyze an algorithm for the sequential estimation of a conditional quantile in the context of real stochastic codes with vectorvalued inputs. Our algorithm is based…
A unified framework for the study of the PLS estimator's properties
Mélanie Blazère, Fabrice Gamboa, Jean-Michel Loubes
In this paper we propose a new approach to study the properties of the Partial Least Squares (PLS) estimator. This approach relies on the link between PLS and discrete orthogonal p…
PLS: a new statistical insight through the prism of orthogonal polynomials
Mélanie Blazère, Fabrice Gamboa, Jean-Michel Loubes
Partial Least Square (PLS) is a dimension reduction method used to remove multicollinearities in a regression model. However contrary to Principal Components Analysis (PCA) the PLS…
Bregman superquantiles. Estimation methods and applications
Tatiana Labopin-Richard, Fabrice Gamboa, Aurélien Garivier +1
In this work, we extend some quantities introduced in "Optimization of conditional value-at-risk" of R.T Rockafellar and S. Uryasev to the case where the proximity between real num…
Group Lasso for generalized linear models in high dimension
Mélanie Blazère, Jean-Michel Loubes, Fabrice Gamboa
Nowadays an increasing amount of data is available and we have to deal with models in high dimension (number of covariates much larger than the sample size). Under sparsity assumpt…
Reliability sensitivity analysis based on probability distribution perturbation with application to CO2 storage
Ekaterina Sergienko, Paul Lemaître, Aurélie Arnaud +2
The objective of reliability sensitivity analysis is to determine input variables that mostly contribute to the variability of the failure probability. In this paper, we study a re…
Functional calibration estimation by the maximum entropy on the mean principle
Santiago Gallón, Jean-Michel Loubes, Fabrice Gamboa
We extend the problem of obtaining an estimator for the finite population mean parameter incorporating complete auxiliary information through calibration estimation in survey sampl…
Spike detection from inaccurate samplings
Jean-Marc Azais, Yohann De Castro, Fabrice Gamboa
This article investigates the support detection problem using the LASSO estimator in the space of measures. More precisely, we study the recovery of a discrete measure (spike train…
Density modification based reliability sensitivity analysis
Paul Lemaître, Ekatarina Sergienko, Aurélie Arnaud +3
Sensitivity analysis of a numerical model, for instance simulating physical phenomena, is useful to quantify the influence of the inputs on the model responses. This paper proposes…
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…
Derivative-based global sensitivity measures: general links with Sobol' indices and numerical tests
Matieyendou Lamboni, Bertrand Iooss, Anne-Laure Popelin +1
The estimation of variance-based importance measures (called Sobol' indices) of the input variables of a numerical model can require a large number of model evaluations. It turns t…
Generalized Hoeffding-Sobol Decomposition for Dependent Variables -Application to Sensitivity Analysis
Gaëlle Chastaing, Fabrice Gamboa, Clémentine Prieur
In this paper, we consider a regression model built on dependent variables. This regression modelizes an input output relationship. Under boundedness assumptions on the joint distr…