36 citations · 36 across the 3 of their papers we have counts for
4 papers
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization
Antonio Candelieri, Riccardo Perego, Francesco Archetti
Searching for accurate Machine and Deep Learning models is a computationally expensive and awfully energivorous process. A strategy which has been gaining recently importance to dr…
Modelling Human Active Search in Optimizing Black-box Functions
Antonio Candelieri, Riccardo Perego, Ilaria Giordani +2
Modelling human function learning has been the subject of in-tense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objec…
Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization
Antonio Candelieri, Ilaria Giordani, Riccardo Perego +1
Bayesian Optimization has become the reference method for the global optimization of black box, expensive and possibly noisy functions. Bayesian Op-timization learns a probabilisti…
Safe global optimization of expensive noisy black-box functions in the -Lipschitz framework
Yaroslav D. Sergeyev, Antonio Candelieri, Dmitri E. Kvasov +1
In this paper, the problem of safe global maximization (it should not be confused with robust optimization) of expensive noisy black-box functions satisfying the Lipschitz conditio…