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Found 4 papers, 0 papers with code
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization
Computational results are reported related to the optimization of the hyperparameters of a Support Vector Machine (SVM) classifier using two sources: a large dataset - the most expensive one - and a smaller portion of it.
Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization
This ap-proach makes more efficient the learning/updating of the probabilistic surrogate model and allows an efficient optimization of the acquisition function.
Modelling Human Active Search in Optimizing Black-box Functions
Modelling human function learning has been the subject of in-tense research in cognitive sciences.
Safe global optimization of expensive noisy black-box functions in the $δ$-Lipschitz framework
The notion "safe" means that the objective function $f(x)$ during optimization should not violate a "safety" threshold, for instance, a certain a priori given value $h$ in a maximization problem.
