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Lipschitz optimisation for Lipschitz Interpolation

Abstract:

Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning that have been proposed to be utilised in the context of system identification and learning-based control. They utilise presupposed Lipschitz properties in order to compute inferences over unobserved function values. Unfortunately, most of these approaches rely on exact knowledge about the input space metric as well as...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.23919/ACC.2017.7963430

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-9003-6642
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Pages:
3141-3146
Publication date:
2017-07-03
Acceptance date:
2017-02-01
DOI:
EISSN:
2378-5861
Pubs id:
pubs:724304
URN:
uri:7117c4c6-a713-46b1-9c56-11f49bf76fc0
UUID:
uuid:7117c4c6-a713-46b1-9c56-11f49bf76fc0
Local pid:
pubs:724304
ISBN:
9781509059928

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