Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
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A4 Artikkeli konferenssijulkaisussa
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Date
2023-04-25
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Language
en
Pages
7425-7454
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Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, Volume 206
Abstract
Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into the optimization process, with the goal of converging towards the global optimum at a reduced cost. This task is generally referred to as multi-fidelity Bayesian optimization (MFBO). However, MFBO algorithms can lead to higher optimization costs than their vanilla BO counterparts, especially when the low-fidelity sources are poor approximations of the objective function, therefore defeating their purpose. To address this issue, we propose rMFBO (robust MFBO), a methodology to make any GP-based MFBO scheme robust to the addition of unreliable information sources. rMFBO comes with a theoretical guarantee that its performance can be bound to its vanilla BO analog, with high controllable probability. We demonstrate the effectiveness of the proposed methodology on a number of numerical benchmarks, outperforming earlier MFBO methods on unreliable sources. We expect rMFBO to be particularly useful to reliably include human experts with varying knowledge within BO processes.Description
This work was supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence FCAI and decision 341763), EU Horizon 2020 (European Network of AI Excellence Centres ELISE, 951847; HumanE AI Net, 952026), UKRI Turing AI World-Leading Researcher Fellowship (EP/W002973/1). We also acknowledge the computational resources provided by the Aalto Science-IT Project from Computer Science IT. | openaire: EC/H2020/951847/EU//ELISE | openaire: EC/H2020/952026/EU//HumanE-AI-Net
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Mikkola, P, Martinelli, J, Filstroff, L & Kaski, S 2023, Multi-Fidelity Bayesian Optimization with Unreliable Information Sources . in Proceedings of the 26th International Conference on Artificial Intelligence and Statistics . Proceedings of Machine Learning Research, vol. 206, JMLR, pp. 7425-7454, International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 25/04/2023 . < https://proceedings.mlr.press/v206/mikkola23a.html >
