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Resilient monotone submodular function maximization

Abstract:

In this paper, we focus on applications in machine learning, optimization, and control that call for the resilient selection of a few elements, e.g. features, sensors, or leaders, against a number of adversarial denial-of-service attacks or failures. In general, such resilient optimization problems are hard, and cannot be solved exactly in polynomial time, even though they often involve objective functions that are monotone and submodular. Notwithstanding, in this paper we provide the first s...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/cdc.2017.8263844

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-0734-5445
Publisher:
Institute of Electrical and Electronics Engineers
Host title:
2017 IEEE 56th Annual Conference on Decision and Control (CDC)
Pages:
1362-1367
Publication date:
2017-12-01
Event title:
2017 IEEE 56th Annual Conference on Decision and Control (CDC)
Event location:
Melbourne, VIC, Australia
Event start date:
2017-12-12
Event end date:
2017-12-15
DOI:
EISBN:
9781509028733
ISBN:
9781509028740
Language:
English
Keywords:
Pubs id:
1095105
Local pid:
pubs:1095105
Deposit date:
2020-03-19

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