BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Learning from Measurements in Exponential Families - Percy Liang (
 University of California\, Berkeley)
DTSTART:20090325T110000Z
DTEND:20090325T120000Z
UID:TALK17569@talks.cam.ac.uk
CONTACT:Simon Lacoste-Julien
DESCRIPTION:Given a model family and unlabeled examples\, what is the most
  cost-effective way of estimating model parameters?  To address this quest
 ion\, we present a Bayesian decision-theoretic framework for learning from
  measurements\, which unifies various labeling schemes with more general c
 onstraints and preferences\, as well as providing a principle for actively
  choosing measurements.  We introduce variational and stochastic approxima
 tions for inference\, which allow us to scale up to real-world sequence la
 beling tasks.
LOCATION:Engineering Department\, CBL Room 438
END:VEVENT
END:VCALENDAR