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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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Publisher copy:
10.1007/978-3-030-00461-3_27

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Department:
Oxford, MPLS, Engineering Science, Oxford-Man Institute
Role:
Author
ORCID:
0000-0002-9003-6642
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
Publisher:
Springer International Publishing
Host title:
SUM 2018: Scalable Uncertainty Management
Series:
Lecture Notes in Computer Science
Journal:
SUM 2018: Scalable Uncertainty Management More from this journal
Volume:
11142
Pages:
373-379
Publication date:
2018-09-11
Acceptance date:
2018-06-13
DOI:
ISSN:
0302-9743
ISBN:
9783030004606
Keywords:
Pubs id:
pubs:934952
UUID:
uuid:1d451f01-740c-486b-ad26-9800e633cf5b
Local pid:
pubs:934952
Source identifiers:
934952
Deposit date:
2019-01-09

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