E
Emmanuel Vazquez
Researcher at Supélec
Publications - 102
Citations - 2084
Emmanuel Vazquez is an academic researcher from Supélec. The author has contributed to research in topics: Gaussian process & Global optimization. The author has an hindex of 17, co-authored 97 publications receiving 1861 citations. Previous affiliations of Emmanuel Vazquez include University of Paris-Sud & Université Paris-Saclay.
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An informational approach to the global optimization of expensive-to-evaluate functions
TL;DR: This paper introduces minimizers entropy as a new Kriging-based criterion for the sequential choice of points at which the function should be evaluated, based on stepwise uncertainty reduction and is extended to robust optimization problems, where both the factors to be tuned and the function evaluations are corrupted by noise.
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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.
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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.
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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.
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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.