Uncertainty in Artificial Intelligence
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Bayesian Optimization with Unknown Constraints
Michael Gelbart, Jasper Snoek, Ryan Adams
Abstract:
Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general case that noise may be present in the constraint func- tions, and the objective and constraints may be evaluated independently. We provide motivating practical examples, and present a general frame- work to solve such problems. We demonstrate the effectiveness of our approach on optimizing the performance of online latent Dirichlet allo- cation subject to topic sparsity constraints, tun- ing a neural network given test-time memory constraints, and optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed time, subject to passing standard convergence di- agnostics.
Keywords:
Pages: 250-259
PS Link:
PDF Link: /papers/14/p250-gelbart.pdf
BibTex:
@INPROCEEDINGS{Gelbart14,
AUTHOR = "Michael Gelbart and Jasper Snoek and Ryan Adams",
TITLE = "Bayesian Optimization with Unknown Constraints",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "250--259"
}


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