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: | , , |
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| מחברים אחרים: | |
| פורמט: | Article |
| שפה: | en_US |
| יצא לאור: |
Association for Computing Machinery
2017
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| גישה מקוונת: | http://hdl.handle.net/1721.1/112929 https://orcid.org/0000-0002-8585-6566 |