Long-run Behaviour of Multi-fidelity Bayesian Optimisation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 19 Dec 2023.
Builds on
10 of 15 references.
References · 10
- 2018A Tutorial on Bayesian Optimizationcited by 734
- 2020Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimizationcited by 176
- 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOcited by 111
- 2016Multi-Information Source Optimizationcited by 83
- 2020HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisationcited by 81
- 2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonflycited by 66
- 2021Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domainscited by 46
- 2022Thinking inside the box: A tutorial on grey-box Bayesian optimizationcited by 26
- 2022Multi-Fidelity Bayesian Optimization with Unreliable Information Sourcescited by 17
- 2021Leveraging Trust for Joint Multi-Objective and Multi-Fidelity Optimizationcited by 7
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Citing papers per year: 2024: 1.
1 paper.