Antonio Candelieri | DeepAI

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Francesco Archetti
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∙ 05/15/2023

Mastering the exploration-exploitation trade-off in Bayesian Optimization

Gaussian Process based Bayesian Optimization is a well-known sample effi...

0 Antonio Candelieri, et al. ∙

∙ 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...

0 Antonio Candelieri, et al. ∙

∙ 10/12/2022

BORA: Bayesian Optimization for Resource Allocation

Optimal resource allocation is gaining a renewed interest due its releva...

0 Antonio Candelieri, et al. ∙

∙ 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...

0 Antonio Candelieri, et al. ∙

∙ 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...

0 Antonio Candelieri, et al. ∙

∙ 10/22/2021

Bayesian Optimization and Deep Learning forsteering wheel angle prediction

Automated driving systems (ADS) have undergone a significant improvement...

0 Alessandro Riboni, et al. ∙

∙ 03/08/2021

Risk Aware Optimization of Water Sensor Placement

Optimal sensor placement (SP) usually minimizes an impact measure, such ...

0 Antonio Candelieri, et al. ∙

∙ 02/09/2021

MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs

This paper addresses black-box optimization over multiple information so...

0 Antonio Candelieri, et al. ∙

∙ 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...

0 Antonio Candelieri, et al. ∙

∙ 03/09/2020

Modelling Human Active Search in Optimizing Black-box Functions

Modelling human function learning has been the subject of in-tense resea...

0 Antonio Candelieri, et al. ∙

∙ 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...

0 Antonio Candelieri, et al. ∙

∙ 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...

0 Yaroslav D. Sergeyev, et al. ∙

∙ 11/09/2018

Reachability-based safe learning for optimal control problem

In this work we seek for an approach to integrate safety in the learning...

0 Stanislav Fedorov, et al. ∙

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