Efficient Bayesian Optimization for Target Vector Estimation

Uhrenholt, A. and Jensen, B. S. (2019) Efficient Bayesian Optimization for Target Vector Estimation. In: 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Okinawa, Japan, 16-18 April 2019, pp. 2661-2670.

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We consider the problem of estimating a target vector by querying an unknown multi-output function which is stochastic and expensive to evaluate. Through sequential experimental design the aim is to minimize the squared Euclidean distance between the output of the function and the target vector. Applying standard single-objective Bayesian optimization to this problem is both wasteful, since individual output components are never observed, and imprecise since the predictive distribution for new inputs will be symmetric and have negative support. We address this issue by proposing a Gaussian process model that considers the individual function outputs and derive a distribution over the resulting 2-norm. Furthermore we derive computationally efficient acquisition functions and evaluate the resulting optimization framework on several synthetic problems and a real-world problem. The results demonstrate a significant improvement over Bayesian optimization based on both standard and warped Gaussian processes.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Uhrenholt, Mr Anders and Jensen, Dr Bjorn
Authors: Uhrenholt, A., and Jensen, B. S.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Proceedings of Machine Learning Research 89:2661-2670
Publisher Policy:Reproduced under a Creative Commons License
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