J
Julien Bect
Researcher at Supélec
Publications - 101
Citations - 1866
Julien Bect is an academic researcher from Supélec. The author has contributed to research in topics: Gaussian process & Bayesian probability. The author has an hindex of 18, co-authored 96 publications receiving 1659 citations. Previous affiliations of Julien Bect include Université Paris-Saclay & French Institute for Research in Computer Science and Automation.
Papers
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Journal ArticleDOI
Sequential design of computer experiments for the estimation of a probability of failure
TL;DR: SUR (stepwise uncertainty reduction) strategies are derived from a Bayesian formulation of the problem of estimating a probability of failure of a function f using a Gaussian process model of f and aim at performing evaluations of f as efficiently as possible to infer the value of the probabilities of failure.
Journal ArticleDOI
Convergence properties of the expected improvement algorithm with fixed mean and covariance functions
Emmanuel Vazquez,Julien Bect +1 more
TL;DR: The first result is that under some mild hypotheses on the covariance function k of the Gaussian process, the expected improvement algorithm produces a dense sequence of evaluation points in the search domain, when the function to be optimized is in the reproducing kernel Hilbert space generated by k.
Book ChapterDOI
A l1-Unified Variational Framework for Image Restoration
TL;DR: Among image restoration literature, there are mainly two kinds of approach, one is based on a process over image wavelet coefficients, as wavelet shrinkage for denoising and the other is based over image gradient, which usually assumes that the image belongs to the space of functions of Bounded Variation.
Journal ArticleDOI
Fast Parallel Kriging-Based Stepwise Uncertainty Reduction With Application to the Identification of an Excursion Set
TL;DR: This article introduces several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel, and manages to drastically reduce the computational cost of these strategies through the use of closed form formulas.
Journal ArticleDOI
A Bayesian approach to constrained single- and multi-objective optimization
TL;DR: An extended domination rule is used to handle objectives and constraints in a unified way, and a corresponding expected hyper-volume improvement sampling criterion is proposed, which is compared to state-of-the-art algorithms for single- and multi-objective constrained optimization.