Title :

Bayesian Optimization using Deep Gaussian Processes

Author(s) :

Hebbal, Ali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
DTIS, ONERA, Université Paris Saclay [Palaiseau]
Brevault, Loic [Auteur]
DTIS, ONERA, Université Paris Saclay [Palaiseau]
Balesdent, Mathieu [Auteur]
DTIS, ONERA, Université Paris Saclay [Palaiseau]
Talbi, El-Ghazali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Melab, Nouredine [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]

English keyword(s) :

Bayesian Optimization · Gaussian Process · Deep Gaussian Process · Non-stationary function · Global Optimization * PhD student
ONERA
DTIS

HAL domain(s) :

Statistiques [stat]
Statistiques [stat]/Machine Learning [stat.ML]
Sciences de l'ingénieur [physics]

English abstract : [en]

Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classic ...
Show more >Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classic Gaussian Processes, this method may not be adapted for non-stationary functions involved in the optimization problem. To overcome this issue, a new Bayesian Optimization approach is proposed. It is based on Deep Gaussian Processes as surrogate models instead of classic Gaussian Processes. This modeling technique increases the power of representation to capture the non-stationarity by simply considering a functional composition of stationary Gaussian Processes, providing a multiple layer structure. This paper proposes a new algorithm for Global Optimization by coupling Deep Gaussian Processes and Bayesian Optimization. The specificities of this optimization method are discussed and highlighted with academic test cases. The performance of the proposed algorithm is assessed on analytical test cases and an aerospace design optimization problem and compared to the state-of-the-art stationary and non-stationary Bayesian Optimization approaches.Show less >

Language :

Anglais

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL