1 code implementation • 6 Feb 2024 • Raphaël Carpintero Perez, Sébastien da Veiga, Josselin Garnier, Brian Staber
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 solving partial differential equations, or predicting material properties.
1 code implementation • NeurIPS 2023 • 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).
no code implementations • 8 Jun 2022 • 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.
1 code implementation • 25 May 2021 • Clément Bénard, Gérard Biau, Sébastien da Veiga, Erwan Scornet
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools.
1 code implementation • 9 Mar 2021 • Reda El Amri, Rodolphe Le Riche, Céline Helbert, Christophette Blanchet-Scalliet, Sébastien da Veiga
The main contribution of this work is an acquisition criterion that accounts for both the average improvement in objective function and the constraint reliability.
no code implementations • 26 Feb 2021 • Clément Bénard, Sébastien da Veiga, Erwan Scornet
Variable importance measures are the main tools to analyze the black-box mechanisms of random forests.
no code implementations • 14 Jan 2021 • Sébastien da Veiga
In particular when the inputs are independent, Sobol' sensitivity indices attribute a portion of the output of interest variance to each input and all possible interactions in the model, thanks to a functional ANOVA decomposition.
Statistics Theory Statistics Theory
no code implementations • 29 Apr 2020 • Clément Bénard, Gérard Biau, Sébastien da Veiga, Erwan Scornet
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules.
no code implementations • 26 Feb 2020 • Sébastien Petit, Julien Bect, Sébastien da Veiga, Paul Feliot, Emmanuel Vazquez
We consider the problem of estimating the parameters of the covariance function of a Gaussian process by cross-validation.
no code implementations • 19 Aug 2019 • Clément Bénard, Gérard Biau, Sébastien da Veiga, Erwan Scornet
State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism.
no code implementations • 30 Nov 2018 • Raphaël Deswarte, Véronique Gervais, Gilles Stoltz, Sébastien da Veiga
An extension of the deterministic aggregation approach is thus proposed in this paper to provide such multi-step-ahead forecasts.
no code implementations • 11 Nov 2013 • 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 multivariate variables in a limited way.