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SUMMARY:Decision Making and Inference under Limited Information and Large 
 Dimensionality - Stefano Ermon\, Cornell University
DTSTART:20140328T100000Z
DTEND:20140328T110000Z
UID:TALK51668@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:Statistical inference in high-dimensional probabilistic models
  (i.e.\, with many variables) is one of the central problems of statistica
 l machine learning and stochastic decision making. To date\, only a handfu
 l of distinct methods have been developed\, most notably (MCMC) sampling\,
  decomposition\, and variational methods. In this talk\, I will introduce 
 a fundamentally new approach based on random projections and combinatorial
  optimization. Our approach provides provable guarantees on accuracy\, and
  outperforms traditional methods in a range of domains\, in particular tho
 se involving combinations of probabilistic and causal dependencies (such a
 s those coming from physical laws) among the variables. This allows for a 
 tighter integration between inductive and deductive reasoning\, and offers
  a range of new modeling opportunities. As an example\, I will discuss an 
 application in the emerging field of Computational Sustainability aimed at
  discovering new fuel-cell materials where we greatly improved the quality
  of the results by incorporating prior background knowledge of the physics
  of the system into the model.
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
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