Conference item
Scalable bounding of predictive uncertainty in regression problems with SLAC
- Abstract:
- We propose SLAC, a sparse approximation to a Lipschitz constant estimator that can be utilised to obtain uncertainty bounds around predictions of a regression method. As we demonstrate in a series of experiments on real-world and synthetic data, this approach can yield fast and robust predictive uncertainty bounds that are as reliable as those of Gaussian Processes or Bayesian Neural Networks, while reducing computational effort markedly.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 374.1KB, Terms of use)
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- Publisher copy:
- 10.1007/978-3-030-00461-3_27
Authors
- Publisher:
- Springer International Publishing
- Host title:
- SUM 2018: Scalable Uncertainty Management
- Journal:
- SUM 2018: Scalable Uncertainty Management More from this journal
- Volume:
- 11142
- Pages:
- 373-379
- Series:
- Lecture Notes in Computer Science
- Publication date:
- 2018-09-11
- Acceptance date:
- 2018-06-13
- DOI:
- ISSN:
-
0302-9743
- ISBN:
- 9783030004606
- Keywords:
- Pubs id:
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pubs:934952
- UUID:
-
uuid:1d451f01-740c-486b-ad26-9800e633cf5b
- Local pid:
-
pubs:934952
- Source identifiers:
-
934952
- Deposit date:
-
2019-01-09
- ARK identifier:
Terms of use
- Copyright holder:
- Springer Nature Switzerland AG
- Copyright date:
- 2018
- Notes:
- Copyright © 2018 Springer Nature Switzerland AG. This is the accepted manuscript version of the paper. The final version is available online from Springer at: https://doi.org/10.1007/978-3-030-00461-3_27
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