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Static prediction games for adversarial learning problems

  • The standard assumption of identically distributed training and test data is violated when the test data are generated in response to the presence of a predictive model. This becomes apparent, for example, in the context of email spam filtering. Here, email service providers employ spam filters, and spam senders engineer campaign templates to achieve a high rate of successful deliveries despite the filters. We model the interaction between the learner and the data generator as a static game in which the cost functions of the learner and the data generator are not necessarily antagonistic. We identify conditions under which this prediction game has a unique Nash equilibrium and derive algorithms that find the equilibrial prediction model. We derive two instances, the Nash logistic regression and the Nash support vector machine, and empirically explore their properties in a case study on email spam filtering.

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Metadaten
Author:Michael Brückner, Christian Kanzow, Tobias Scheffer
ISSN:1532-4435 (print)
Parent Title (English):Journal of machine learning research
Publisher:Microtome Publishing
Place of publication:Cambridge, Mass.
Document Type:Article
Language:English
Year of first Publication:2012
Year of Completion:2012
Release Date:2017/03/26
Tag:Nash equilibrium; adversarial classification; static prediction games
Volume:13
Pagenumber:38
First Page:2617
Last Page:2654
Funder:German Science Foundation (DFG) [SCHE540/12-1]; STRATO AG
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science
Peer Review:Referiert
Publication Way:Open Access
Institution name at the time of publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik