Bayesian optimization with a finite budget: An approximate dynamic programming approach
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6188-bayesian-optimization-with-a-finite-budget-an-approximate-dynamic-programming-approach.pdf
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402.78 KB
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Author(s)
Wolpert, David H.
Lam, Remi Roger Alain Paul
Willcox, Karen E
Date Issued
December 2016
Journal
Advances in Neural Information Processing Systems 29 (NIPS 2016)
Publisher
Neural Information Processing Systems Foundation
Citation
Lam, Rem, Karen Willcox, and David H. Wolpert. "Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach." Advances in Neural Information Processing Systems 29 (NIPS 2016), 5-12 December, 2016, Barcelona, Spain, Neural Information Processing Systems Foundation, 2016. © 2016 NIPS Foundation
Version
Final published version
Abstract
We consider the problem of optimizing an expensive objective function when a finite budget of total evaluations is prescribed. In that context, the optimal solution strategy for Bayesian optimization can be formulated as a dynamic programming instance. This results in a complex problem with uncountable, dimension-increasing state space and an uncountable control space. We show how to approximate the solution of this dynamic programming problem using rollout, and propose rollout heuristics specifically designed for the Bayesian optimization setting. We present numerical experiments showing that the resulting algorithm for optimization with a finite budget outperforms several popular Bayesian optimization algorithms.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/6188-bayesian-optimization-with-a-finite-budget-an-approximate-dynamic-programming-approach
