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Online metric algorithms with untrusted predictions

Abstract:

Machine-learned predictors, although achieving very good results for inputs resembling training data, cannot possibly provide perfect predictions in all situations. Still, decision-making systems that are based on such predictors need not only to benefit from good predictions but also to achieve a decent performance when the predictions are inadequate. In this paper, we propose a prediction setup for arbitrary metrical task systems (MTS) (e.g., caching, k-server and convex body chasing) and o...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
St Anne's College
Role:
Author
ORCID:
0000-0003-3744-0977
Publisher:
Journal of Machine Learning Research
Series:
Proceedings of Machine Learning Research
Series number:
119
Pages:
322-332
Publication date:
2020-11-21
Event title:
37th International Conference on Machine Learning (ICML 2020)
Event location:
Virtual event
Event website:
https://icml.cc/Conferences/2020
Event start date:
2020-07-13
Event end date:
2020-07-18
ISSN:
2640-3498
ISBN:
9781713821120
Language:
English
Keywords:
Pubs id:
1308703
Local pid:
pubs:1308703
Deposit date:
2023-03-28

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