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UID:322@lincs.fr
DTSTART;TZID=Europe/Paris:20170927T140000
DTEND;TZID=Europe/Paris:20170927T150000
DTSTAMP:20171003T111720Z
URL:https://www.lincs.fr/events/active-hypothesis-testing-learning-to-matc
 h-in-expert-systems/
SUMMARY:Active Hypothesis Testing: Learning to Match in Expert Systems
DESCRIPTION:We study adaptive matching in expert systems\, as an instance
 of adaptive sequential hypothesis testing. Examples of such systems include
 Q&amp\;A platforms\, crowdsourcing\, image classification. Consider a
 system that receives tasks or jobs to be classified into one of a set of
 given types. The system has access to a set of workers\, or experts\, and
 the expertise of a worker is defined by the jobs he is able to classify and
 the error in his response. This active sequential hypothesis testing
 problem was first addressed by Chernoff in 1959\, whereby experts to be
 queried are selected according to how much information they provide. In
 this talk we will begin with an overview of past work on this topic\, then
 consider our model where we assume access to less fine-grained information
 about the expertise of workers. We propose a gradient-based algorithm\,
 show its optimality and through numerical results show that it outperforms
 the Chernoff-like algorithms.
CATEGORIES:Seminars
LOCATION:LINCS Seminars room\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.828400;2.356897
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 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS Seminars
 room:geo:48.828400,2.356897
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TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
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DTSTART:20170326T030000
TZOFFSETFROM:+0100
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