Non-Myopic Multifidelity Bayesian Optimization

• URL: http://arxiv.org/abs/2207.06325v3
• Date: Thu, 4 Jul 2024 14:50:57 GMT
• Title: Non-Myopic Multifidelity Bayesian Optimization
• Authors: Francesco Di Fiore, Laura Mainini,
• Abstract summary: This paper proposes a non-myopic multifidelity Bayesian framework to grasp the long-term reward from future steps of the optimization. We demonstrate that the proposed algorithm outperforms a standard multifidelity Bayesian framework on popular benchmark optimization problems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelity representations of expensive objective functions. Popular multifidelity Bayesian strategies rely on sampling policies that account for the immediate reward obtained evaluating the objective function at a specific input, precluding greater informative gains that might be obtained looking ahead more steps. This paper proposes a non-myopic multifidelity Bayesian framework to grasp the long-term reward from future steps of the optimization. Our computational strategy comes with a two-step lookahead multifidelity acquisition function that maximizes the cumulative reward obtained measuring the improvement in the solution over two steps ahead. We demonstrate that the proposed algorithm outperforms a standard multifidelity Bayesian framework on popular benchmark optimization problems.

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