Multi-Fidelity Bayesian Optimization with Unreliable Information Sources
| Autor: | Mikkola, Petrus, Martinelli, Julien, Filstroff, Louis, Kaski, Samuel |
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| Přispěvatelé: | Department of Computer Science, Probabilistic Machine Learning, Computer Science Professors, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Popis: | 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. Comment: Accepted for publication at AISTATS 2023. Code available at https://github.com/AaltoPML/rMFBO |
| Databáze: | OpenAIRE |
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