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Journal article

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 benefit from good predictions, but should also achieve a decent performance when the predictions are inadequate. In this article, we propose a prediction setup for arbitrary metrical task systems (MTS) (e.g., caching, k-server, and convex body chasing) ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1145/3582689

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
Publisher:
Association for Computing Machinery
Journal:
ACM Transactions on Algorithms More from this journal
Volume:
19
Issue:
2
Article number:
19
Publication date:
2023-04-15
Acceptance date:
2023-01-18
DOI:
ISSN:
1549-6325
Language:
English
Keywords:
Pubs id:
1337619
Local pid:
pubs:1337619
Deposit date:
2023-04-17

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