50 citations · 118 across the 7 of their papers we have counts for
4 papers · 1 filter
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.…
Nested Kriging predictions for datasets with large number of observations
Didier Rullière, Nicolas Durrande, François Bachoc +1
This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpola…
Differentiating the multipoint Expected Improvement for optimal batch design
Sébastien Marmin, Clément Chevalier, David Ginsbourger
This work deals with parallel optimization of expensive objective functions which are modeled as sample realizations of Gaussian processes. The study is formalized as a Bayesian op…
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…