Search Results for author:
Found 5 papers, 1 papers with code
Adaptive Design of Experiments for Conservative Estimation of Excursion Sets
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
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
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.
Differentiating the multipoint Expected Improvement for optimal batch design
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.
Quantifying uncertainties on excursion sets under a Gaussian random field prior
In this setting, the posterior distribution on the objective function gives rise to a posterior distribution on excursion sets.
