7 papers
Resilience in urban networked infrastructure: the case of Water Distribution Systems
Antonio Candelieri, Ilaria Giordani, Andrea Ponti +1
Resilience is meant as the capability of a networked infrastructure to provide its service even if some components fail: in this paper we focus on how resilience depends both on ne…
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…
Learning Optimal Control of Water Distribution Networks through Sequential Model-based Optimization
Antonio Candelieri, Bruno Galuzzi, Ilaria Giordani +1
Sequential Model-based Bayesian Optimization has been successful-ly applied to several application domains, characterized by complex search spaces, such as Automated Machine Learni…
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…