Search Results for author: David Ginsbourger

Found 18 papers, 2 papers with code

Non-Sequential Ensemble Kalman Filtering using Distributed Arrays

no code implementations21 Nov 2023 Cédric Travelletti, Jörg Franke, David Ginsbourger, Stefan Brönnimann

This work introduces a new, distributed implementation of the Ensemble Kalman Filter (EnKF) that allows for non-sequential assimilation of large datasets in high-dimensional problems.

Distributed Computing

Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes

no code implementations11 Oct 2023 Philip Stange, David Ginsbourger

We tackle the extension to the vector-valued case of consistency results for Stepwise Uncertainty Reduction sequential experimental design strategies established in [Bect et al., A supermartingale approach to Gaussian process based sequential design of experiments, Bernoulli 25, 2019].

Experimental Design Gaussian Processes

Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures

no code implementations15 Jun 2022 Johanna Ziegel, David Ginsbourger, Lutz Dümbgen

We present new classes of positive definite kernels on non-standard spaces that are integrally strictly positive definite or characteristic.

Fast ABC with joint generative modelling and subset simulation

no code implementations16 Apr 2021 Eliane Maalouf, David Ginsbourger, Niklas Linde

We propose a novel approach for solving inverse-problems with high-dimensional inputs and an expensive forward mapping.

Geophysics

Goal-oriented adaptive sampling under random field modelling of response probability distributions

no code implementations15 Feb 2021 Athénaïs Gautier, David Ginsbourger, Guillaume Pirot

In the study of natural and artificial complex systems, responses that are not completely determined by the considered decision variables are commonly modelled probabilistically, resulting in response distributions varying across decision space.

Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances

no code implementations8 Jan 2021 David Ginsbourger, Cedric Schärer

We further establish in the case of noiseless observations that correcting for covariances between residuals in cross-validation-based estimation of the scale parameter leads back to MLE.

Learning from demonstration with model-based Gaussian process

no code implementations11 Oct 2019 Noémie Jaquier, David Ginsbourger, Sylvain Calinon

In learning from demonstrations, it is often desirable to adapt the behavior of the robot as a function of the variability retrieved from human demonstrations and the (un)certainty encoded in different parts of the task.

Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization

no code implementations9 Oct 2019 Poompol Buathong, David Ginsbourger, Tipaluck Krityakierne

We investigate two classes of set kernels that both rely on Reproducing Kernel Hilbert Space embeddings, namely the ``Double Sum'' (DS) kernels recently considered in Bayesian set optimization, and a class introduced here called ``Deep Embedding'' (DE) kernels that essentially consists in applying a radial kernel on Hilbert space on top of the canonical distance induced by another kernel such as a DS kernel.

Combinatorial Optimization

On the choice of the low-dimensional domain for global optimization via random embeddings

1 code implementation18 Apr 2017 Mickaël Binois, David Ginsbourger, Olivier Roustant

Then, the search of solutions can be reduced to the random embedding of a low dimensional space into the original one, resulting in a more manageable optimization problem.

Bayesian Optimization

Adaptive Design of Experiments for Conservative Estimation of Excursion Sets

no code implementations22 Nov 2016 Dario Azzimonti, David Ginsbourger, Clément Chevalier, Julien Bect, Yann Richet

The system is modeled by an expensive-to-evaluate function, such as a computer experiment, and we are interested in its excursion set, i. e. the set of points where the function takes values above or below some prescribed threshold.

Efficient batch-sequential Bayesian optimization with moments of truncated Gaussian vectors

no code implementations9 Sep 2016 Sébastien Marmin, Clément Chevalier, David Ginsbourger

We deal with the efficient parallelization of Bayesian global optimization algorithms, and more specifically of those based on the expected improvement criterion and its variants.

Bayesian Optimization

A supermartingale approach to Gaussian process based sequential design of experiments

no code implementations3 Aug 2016 Julien Bect, François Bachoc, David Ginsbourger

Thisobservation enables us to establish generic consistency results for abroad class of SUR strategies.

Differentiating the multipoint Expected Improvement for optimal batch design

no code implementations18 Mar 2015 Sébastien Marmin, Clément Chevalier, David Ginsbourger

The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms.

Bayesian Optimization Gaussian Processes

Quantifying uncertainties on excursion sets under a Gaussian random field prior

no code implementations15 Jan 2015 Dario Azzimonti, Julien Bect, Clément Chevalier, David Ginsbourger

In this setting, the posterior distribution on the objective function gives rise to a posterior distribution on excursion sets.

A warped kernel improving robustness in Bayesian optimization via random embeddings

no code implementations13 Nov 2014 Mickaël Binois, David Ginsbourger, Olivier Roustant

This works extends the Random Embedding Bayesian Optimization approach by integrating a warping of the high dimensional subspace within the covariance kernel.

Bayesian Optimization

Invariances of random fields paths, with applications in Gaussian Process Regression

no code implementations6 Aug 2013 David Ginsbourger, Olivier Roustant, Nicolas Durrande

We study pathwise invariances of centred random fields that can be controlled through the covariance.

regression

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