Zobrazeno 1 - 10
of 68
pro vyhledávání: '"da Veiga, Sébastien"'
In computational physics, machine learning has now emerged as a powerful complementary tool to explore efficiently candidate designs in engineering studies. Outputs in such supervised problems are signals defined on meshes, and a natural question is
Externí odkaz:
http://arxiv.org/abs/2410.15721
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.
Externí odkaz:
http://arxiv.org/abs/2402.03838
In this work, we develop an approach mentioned by da Veiga and Gamboa in 2013. It consists in extending the very interestingpoint of view introduced in \cite{gine2008simple} to estimate general nonlinear integral functionals of a density on the real
Externí odkaz:
http://arxiv.org/abs/2303.17832
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradi
Externí odkaz:
http://arxiv.org/abs/2301.13528
Autor:
Staber, Brian, Da Veiga, Sébastien
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. Despite the growing literature about uncertainty quantification in deep learn
Externí odkaz:
http://arxiv.org/abs/2206.06779
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects are now widely used to interpret both tree ensembles and neural network
Externí odkaz:
http://arxiv.org/abs/2105.11724
Autor:
Amri, Reda El, Riche, Rodolphe Le, Helbert, Céline, Blanchet-Scalliet, Christophette, Da Veiga, Sébastien
We consider the problem of chance constrained optimization where it is sought to optimize a function and satisfy constraints, both of which are affected by uncertainties. The real world declinations of this problem are particularly challenging becaus
Externí odkaz:
http://arxiv.org/abs/2103.05706
Variable importance measures are the main tools to analyze the black-box mechanisms of random forests. Although the mean decrease accuracy (MDA) is widely accepted as the most efficient variable importance measure for random forests, little is known
Externí odkaz:
http://arxiv.org/abs/2102.13347
Autor:
da Veiga, Sébastien
Global sensitivity analysis is the main quantitative technique for identifying the most influential input variables in a numerical simulation model. In particular when the inputs are independent, Sobol' sensitivity indices attribute a portion of the
Externí odkaz:
http://arxiv.org/abs/2101.05487
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. State-of-the-art learning algorithms are often referred to as "black boxes" because of t
Externí odkaz:
http://arxiv.org/abs/2004.14841