Multi-fidelity Bayesian Optimisation (MFBO) has been shown to generally converge faster than single-fidelity Bayesian Optimisation (SFBO) (Poloczek et al. (2017)). Inspired by recent benchmark papers, we are investigating the long-run behaviour of MFBO, based on observations in the literature that it might under-perform in certain scenarios (Mikkola et al. (2023), Eggensperger et al. (2021)). An under-performance of MBFO in the long-run could significantly undermine its application to many research tasks, especially when we are not able to identify when the under-performance begins. We create a simple benchmark study, showcase empirical results and discuss scenarios and possible reasons of under-performance.
@article{arxiv.2312.12633,
title = {Long-run Behaviour of Multi-fidelity Bayesian Optimisation},
author = {Gbetondji J-S Dovonon and Jakob Zeitler},
journal= {arXiv preprint arXiv:2312.12633},
year = {2023}
}
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NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World