Gaussian Process Planning with Lipschitz Continuous Reward Functions
This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian optimization criteria and offer practitioners some flexibility to specify their desi...
Main Authors: | Ling, Chun Kai, Low, Kian Hsiang, Jaillet, Patrick |
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Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | en_US |
Published: |
Association for Computing Machinery
2017
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Online Access: | http://hdl.handle.net/1721.1/112929 https://orcid.org/0000-0002-8585-6566 |
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