Bayesian Optimization with Unknown Search Space
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Publication:6328071
arXiv1910.13092MaRDI QIDQ6328071
Author name not available (Why is that?)
Publication date: 29 October 2019
Abstract: Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Bayesian optimization. We devise a strategy to guarantee that in iterative expansions of the search space, our method can find a point whose function value within epsilon of the objective function maximum. Without the need to specify any parameters, our algorithm automatically triggers a minimal expansion required iteratively. We derive analytic expressions for when to trigger the expansion and by how much to expand. We also provide theoretical analysis to show that our method achieves epsilon-accuracy after a finite number of iterations. We demonstrate our method on both benchmark test functions and machine learning hyper-parameter tuning tasks and demonstrate that our method outperforms baselines.
Has companion code repository: https://github.com/HuongHa12/BO_unknown_searchspace
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