Multi-Fidelity Bayesian Optimization with Unreliable Information Sources

• URL: http://arxiv.org/abs/2210.13937v1
• Date: Tue, 25 Oct 2022 11:47:33 GMT
• Title: Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
• Authors: Petrus Mikkola, Julien Martinelli, Louis Filstroff, Samuel Kaski
• Abstract summary: We propose rMFBO (robust MFBO) to make GP-based MFBO schemes robust to the addition of unreliable information sources. We demonstrate the effectiveness of the proposed methodology on a number of numerical benchmarks. We expect rMFBO to be particularly useful to reliably include human experts with varying knowledge within BO processes.
• Score: 12.509709549771385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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