6 citations · 18 across the 5 of their papers we have counts for
5 papers
Disintegration of Gaussian Measures for Sequential Assimilation of Linear Operator Data
Cédric Travelletti, David Ginsbourger
Gaussian processes appear as building blocks in various stochastic models and have been found instrumental to account for imprecisely known, latent functions. It is often the case…
Efficient batch-sequential Bayesian optimization with moments of truncated Gaussian vectors
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.…
A warped kernel improving robustness in Bayesian optimization via random embeddings
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. The proposed warping,…
On ANOVA decompositions of kernels and Gaussian random field paths
David Ginsbourger, Olivier Roustant, Dominic Schuhmacher +2
The FANOVA (or "Sobol'-Hoeffding") decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objecti…
Corrected Kriging update formulae for batch-sequential data assimilation
Clément Chevalier, David Ginsbourger
Recently, a lot of effort has been paid to the efficient computation of Kriging predictors when observations are assimilated sequentially. In particular, Kriging update formulae en…