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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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01/31/2023
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022...
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05/25/2021
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Interpretability of learning algorithms is crucial for applications invo...
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03/09/2021
A sampling criterion for constrained Bayesian optimization with uncertainties
We consider the problem of chance constrained optimization where it is s...
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02/26/2021
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
Variable importance measures are the main tools to analyze the black-box...
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01/14/2021
Kernel-based ANOVA decomposition and Shapley effects – Application to global sensitivity analysis
Global sensitivity analysis is the main quantitative technique for ident...
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04/29/2020
Interpretable Random Forests via Rule Extraction
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a...
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02/26/2020
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients
We consider the problem of estimating the parameters of the covariance f...
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08/19/2019
SIRUS: making random forests interpretable
State-of-the-art learning algorithms, such as random forests or neural n...
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11/12/2018
Global sensitivity analysis for optimization with variable selection
The optimization of high dimensional functions is a key issue in enginee...
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11/11/2013