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SUMMARY:Politics\, Preferences and Permutations: Probabilistic Reasoning w
 ith Rankings - Jonathan Huang
DTSTART:20110414T083000Z
DTEND:20110414T093000Z
UID:TALK30769@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:Permutations arise fundamentally in a plethora of real world a
 pplications from multi-person tracking to preference ranking and election 
 analysis.  Real world data\, often being noisy and incomplete\, necessitat
 es a probabilistic approach to learning and reasoning with permutations.  
 However\, representing arbitrary probability distributions over the space 
 of permutations has been notoriously intractable due to the factorial numb
 er of permutations.\n\nIn this talk\, I will present methods for efficient
 ly representing and reasoning with such distributions.\nThe main idea that
  I set forth is that distributions over permutations can be decomposed add
 itively or multiplicatively into a series of simpler functions which can b
 e dealt with more easily.  As I show\, additive decompositions turn out to
  correspond to generalized Fourier analysis on the symmetric groups\, whil
 e multiplicative decompositions correspond to a generalized notion of prob
 abilistic independence.\nAlong the way\, I will discuss applications of th
 ese methods for statistically analyzing political elections in Ireland\, p
 reference surveys for sushi\, as well as for performing multi-person track
 ing using a networked array of cameras.\n\nBiography:\nJonathan Huang is a
  Ph.D. candidate in the School of Computer Science at Carnegie Mellon Univ
 ersity where he also received a Masters degree in 2008.\nHe received his B
 .S. degree in Mathematics from Stanford University in 2005.  His research 
 interests lie primarily in statistical machine learning and for his disser
 tation\, he has developed efficient statistical techniques for modeling an
 d performing inference with combinatorial objects such as permutations and
  rankings.\nHis research has resulted in a number of publications in premi
 er machine learning conferences and journals\, receiving a paper award in 
 NIPS 2007 for his work on applying group theoretic Fourier analysis to pro
 babilistic reasoning with permutations.
LOCATION:Small lecture theatre\, Microsoft Research Ltd\, 7 J J Thomson Av
 enue (Off Madingley Road)\, Cambridge
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