English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We deal with the efficient parallelization of Bayesian global optimizationalgorithms, and more specifically of those based on the expected improvementcriterion and its variants. A closed form formula relying on multivariateGaussian cumulative distribution functions is established for a generalizedversion of the multipoint expected improvement criterion. In turn, the latterrelies on intermediate results that could be of independent interest concerningmoments of truncated Gaussian vectors. The obtained expansion of the criterionenables studying its differentiability with respect to point batches andcalculating the corresponding gradient in closed form. Furthermore , we derivefast numerical approximations of this gradient and propose efficient batchoptimization strategies. Numerical experiments illustrate that the proposedapproaches enable computational savings of between one and two order ofmagnitudes, hence enabling derivative-based batch-sequential acquisitionfunction maximization to become a practically implementable and efficientstandard.

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