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05/09/2023
Estimation of large covariance matrices via free deconvolution: computational and statistical aspects
The estimation of large covariance matrices has a high dimensional bias....
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03/31/2023
New estimation of Sobol' indices using kernels
In this work, we develop an approach mentioned by da Veiga and Gamboa in...
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01/06/2023
On the coalitional decomposition of parameters of interest
Understanding the behavior of a black-box model with probabilistic input...
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09/23/2022
Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models
Robustness studies of black-box models is recognized as a necessary task...
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07/29/2022
Bayesian quadrature for H^1(μ) with Poincaré inequality on a compact interval
Motivated by uncertainty quantification of complex systems, we aim at fi...
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07/01/2021
Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions
Variance-based global sensitivity analysis, in particular Sobol' analysi...
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03/08/2021
Fairness seen as Global Sensitivity Analysis
Ensuring that a predictor is not biased against a sensible feature is th...
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11/22/2020
Weighted lens depth: Some applications to supervised classification
Starting with Tukey's pioneering work in the 1970's, the notion of depth...
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09/08/2020
Dual optimal design and the Christoffel-Darboux polynomial
The purpose of this short note is to show that the Christoffel-Darboux p...
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03/03/2020
Global Sensitivity Analysis: a new generation of mighty estimators based on rank statistics
We propose a new statistical estimation framework for a large family of ...
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02/11/2020
Sensitivity analysis in general metric spaces
In this paper, we introduce new indices adapted to outputs valued in gen...
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01/22/2019
Optimal Uncertainty Quantification of a risk measurement from a thermal-hydraulic code using Canonical Moments
We study an industrial computer code related to nuclear safety. A major ...
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11/30/2018
Optimal Uncertainty Quantification on moment class using canonical moments
We gain robustness on the quantification of a risk measurement by accoun...
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10/18/2018
Entropic Variable Boosting for Explainability and Interpretability in Machine Learning
In this paper, we present a new explainability formalism to make clear t...
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06/08/2018
Obtaining fairness using optimal transport theory
Statistical algorithms are usually helping in making decisions in many a...
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03/16/2018
Gaussian Processes indexed on the symmetric group: prediction and learning
In the framework of the supervised learning of a real function defined o...
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10/02/2017
Deep Learning applied to Road Traffic Speed forecasting
In this paper, we propose deep learning architectures (FNN, CNN and LSTM...
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01/31/2017
Gaussian Process Regression Model for Distribution Inputs
Monge-Kantorovich distances, otherwise known as Wasserstein distances, h...
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10/08/2012