Zobrazeno 1 - 10
of 223
pro vyhledávání: '"Vazquez, Emmanuel"'
Autor:
Pion, Aurélien, Vazquez, Emmanuel
Publikováno v:
LOD 2024, 10th International Conference on Machine Learning, Optimization, and Data Science, Sep 2024, Castiglione della Pescaia Grosseto Italy, Italy
This article advocates the use of conformal prediction (CP) methods for Gaussian process (GP) interpolation to enhance the calibration of prediction intervals. We begin by illustrating that using a GP model with parameters selected by maximum likelih
Externí odkaz:
http://arxiv.org/abs/2407.08271
We consider an unknown multivariate function representing a system-such as a complex numerical simulator-taking both deterministic and uncertain inputs. Our objective is to estimate the set of deterministic inputs leading to outputs whose probability
Externí odkaz:
http://arxiv.org/abs/2211.01008
Autor:
Barracosa, Bruno, Bect, Julien, Baraffe, Héloïse Dutrieux, Morin, Juliette, Fournel, Josselin, Vazquez, Emmanuel
This article focuses on the multi-objective optimization of stochastic simulators with high output variance, where the input space is finite and the objective functions are expensive to evaluate. We rely on Bayesian optimization algorithms, which use
Externí odkaz:
http://arxiv.org/abs/2207.03842
This work presents a new procedure for obtaining predictive distributions in the context of Gaussian process (GP) modeling, with a relaxation of the interpolation constraints outside some ranges of interest: the mean of the predictive distributions n
Externí odkaz:
http://arxiv.org/abs/2206.03034
Publikováno v:
53\`emes Journ\'ees de Statistique de la SFdS, June 2022, Lyon, France
In this article we revisit the problem of numerical integration for monotone bounded functions, with a focus on the class of nonsequential Monte Carlo methods. We first provide new a lower bound on the maximal $L^p$ error of nonsequential algorithms,
Externí odkaz:
http://arxiv.org/abs/2203.00423
Autor:
Basak, Subhasish, Guillier, Laurent, Bect, Julien, Christy, Janushan, Tenenhaus-Aziza, Fanny, Vazquez, Emmanuel
Publikováno v:
In Microbial Risk Analysis December 2024 27-28
This article revisits the fundamental problem of parameter selection for Gaussian process interpolation. By choosing the mean and the covariance functions of a Gaussian process within parametric families, the user obtains a family of Bayesian procedu
Externí odkaz:
http://arxiv.org/abs/2107.06006
Publikováno v:
14th World Congress in Computational Mechanics and ECCOMAS Congress 2020 (WCCM-ECCOMAS), Jan 2021, Virtual conference, originally scheduled in Paris, France
Numerical models based on partial differential equations (PDE), or integro-differential equations, are ubiquitous in engineering and science, making it possible to understand or design systems for which physical experiments would be expensive-sometim
Externí odkaz:
http://arxiv.org/abs/2103.14559
This article investigates the origin of numerical issues in maximum likelihood parameter estimation for Gaussian process (GP) interpolation and investigates simple but effective strategies for improving commonly used open-source software implementati
Externí odkaz:
http://arxiv.org/abs/2101.09747
Autor:
Stroh, Rémi, Bect, Julien, Demeyer, Séverine, Fischer, Nicolas, Marquis, Damien, Vazquez, Emmanuel
Publikováno v:
Technometrics, 2022, 64(2):199-209
This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under stud
Externí odkaz:
http://arxiv.org/abs/2007.13553