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UID:107@lincs.fr
DTSTART;TZID=Europe/Paris:20150311T140000
DTEND;TZID=Europe/Paris:20150311T150000
DTSTAMP:20170313T171202Z
URL:https://www.lincs.fr/events/game-theoretic-statistics-learning-data-ge
 nerated-strategic-agents/
SUMMARY:Game-theoretic statistics: Learning from data generated by
 strategic agents
DESCRIPTION:Statistical learning methods developed in the last decades have
 proved very useful for many applications. However\, most algorithms were
 developed under the assumption that the data is independent from the
 algorithm. This is no longer true in applications where data is generated
 or provided by (human) strategic agents. As more and more modern
 applications indeed learn from data provided by external parties\, it
 becomes increasingly crucial to account for the data-provider's incentives
 in order to design well-performing learning algorithms in practice.In this
 talk\, we use game theory to model explicitly the economic incentives of
 the learner and of the agents generating data. We show how\, by analyzing
 the game\, it is possible to design learning algorithms that work better
 because they account for the agents' incentives. Specifically\, we
 illustrate this in two examples with applications in security and privacy:
 (i) Classification of malicious behavior: In this example\, a defender runs
 a classification algorithm to detect malicious behavior while an attacker
 chooses his attack strategy to balance the utility of the attack and the
 probability of being detected. We obtain analytic results that show how to
 optimally perform the classification given the attacker's utility. (ii)
 Linear regression from agents-provided data: In this example\, we study the
 case of an analyst who tries to infer a model through linear regression
 from data provided by individuals who add noise to protect their privacy
 (this technique is called "local privacy"). We again derive results on
 optimal linear regression and on the price of anarchy of the system that
 provide methods to better learn from privacy-conscious users by
 incentivizing them to provide higher-quality data.
CATEGORIES:Seminars,Youtube
LOCATION:LINCS Meeting Room 40\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.8283983;2.3568972000000485
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TZID:Europe/Paris
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DTSTART:20141026T020000
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TZOFFSETTO:+0100
TZNAME:CET
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