research
∙
05/15/2023
Mastering the exploration-exploitation trade-off in Bayesian Optimization
Gaussian Process based Bayesian Optimization is a well-known sample effi...
share
research
∙
12/02/2022
Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels
Gaussian Process regression is a kernel method successfully adopted in m...
research
∙
10/12/2022
BORA: Bayesian Optimization for Resource Allocation
Optimal resource allocation is gaining a renewed interest due its releva...
research
∙
05/18/2022
Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization
There is a consensus that focusing only on accuracy in searching for opt...
research
∙
12/12/2021
Gamifying optimization: a Wasserstein distance-based analysis of human search
The main objective of this paper is to outline a theoretical framework t...
research
∙
10/22/2021
Bayesian Optimization and Deep Learning forsteering wheel angle prediction
Automated driving systems (ADS) have undergone a significant improvement...
research
∙
03/08/2021
Risk Aware Optimization of Water Sensor Placement
Optimal sensor placement (SP) usually minimizes an impact measure, such ...
research
∙
02/09/2021
MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs
This paper addresses black-box optimization over multiple information so...
research
∙
06/25/2020
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization
Searching for accurate Machine and Deep Learning models is a computation...
research
∙
03/09/2020
Modelling Human Active Search in Optimizing Black-box Functions
Modelling human function learning has been the subject of in-tense resea...
research
∙
03/09/2020
Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization
Bayesian Optimization has become the reference method for the global opt...
research
∙
08/15/2019
Safe global optimization of expensive noisy black-box functions in the δ-Lipschitz framework
In this paper, the problem of safe global maximization (it should not be...
research
∙
11/09/2018