An empirical analysis of entropy search in batch bayesian optimisation

A comprehensive study of function shape, batch size, noise level, and dimensionality impact on information-theoretic methods

Bachelor Thesis (2023)
Author(s)

P.A. Hautelman (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

J.A. de Vries – Mentor (TU Delft - Algorithmics)

MTJ Spaan – Mentor (TU Delft - Algorithmics)

Christoph Lofi – Graduation committee member (TU Delft - Web Information Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2023 Alex Hautelman
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Alex Hautelman
Graduation Date
28-06-2023
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Bayesian optimisation is a rapidly growing area of research that aims to identify the optimum of the black-box function, as it strategically directs the optimisation process towards promising regions. This paper provides an overview of the theoretical background used by the Entropy Search algorithms under study, mainly Predictive Entropy Search, Max-Value Entropy Search, and Joint Entropy Search. Furthermore, we empirically analyse the performance and sensitivity of the algorithms in different environment settings. In particular, we discuss the impact of function shape, batch size, noise level, and the number of input dimensions on the final simple regret metric. The results show the weak spots of the information-theoretic methods. However, the algorithms perform better for batch optimisation, demonstrating the advantage when considering the information on the maximum function value.

Files

CSE3000_Final_Paper_vf.pdf
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