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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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Association for Computing Machinery,
2017-12-22T14:55:33Z.
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| Subjects: | |
| Online Access: | Get fulltext |