The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Near-optimal sensor placements in Gaussian processes
Open accessAbstract
When monitoring spatial phenomena, which are often modeled as Gaussian Processes (GPs), choosing sensor locations is a fundamental task. A common strategy is to place sensors at the points of highest entropy (variance) in the GP model. We propose a mutual information criteria, and show that it produces better placements. Furthermore, we prove that finding the configuration that maximizes mutual information is NP-complete. To address this issue, we describe a polynomial-time approximation that is within (1 -- 1/e) of the optimum by exploiting the submodularity of our criterion. This algorithm is extended to handle local structure in the GP, yielding significant speedups. We demonstrate the advantages of our approach on two real-world data sets.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Cited by
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
Settles, B. (2012) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00429ed1v01y201207aim018 · Cited by 605
Bayesian Active Learning for Classification and Preference Learning
Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011) · arXiv · doi:10.48550/arxiv.1112.5745 · Cited by 507
“[Guestrin et al., 2005, Krause et al., 2006] also use mutual information.”
Navigating the protein fitness landscape with Gaussian processes
Romero, P. A., Krause, A., & Arnold, F. H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215251110 · Cited by 360
Self-citationBayesian active probabilistic classification for psychometric field estimation
Song, X. D., Sukesan, K. A., & Barbour, D. L. (2018) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-017-1460-0 · Cited by 22
Metadata
- Authors
- Guestrin, C., Krause, A., & Singh, A. P.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 265-272
- OpenAlex
- W1999824796
- Cited by
- 454
- References
- 1
- FWCI
- 13.36
- Type
- conference paper
- Language
- en
- Open access
- green
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