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CATEGORIES:Machine Learning @ CUED
SUMMARY:Variational Inference for Non-Conjugate Models - D
r Guillaume Bouchard (Xerox)
DTSTART;TZID=Europe/London:20111026T110000
DTEND;TZID=Europe/London:20111026T120000
UID:TALK33878AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/33878
DESCRIPTION:Many statistical techniques\, such as the computat
ion of the data likelihood in the presence of nuis
ance parameters\, or the prediction in the presenc
e of missing data\, the computation of the posteri
or distribution over parameters can be simply expr
essed as high dimensional integration problems for
which standard numerical approximation tools cann
ot be\ndirectly applied. The first part of the tal
k will introduce Split Variational Inference\, a g
eneric way of computing large scale non-Gaussian i
ntegrals by splitting them into a sum of small pie
ces that are easier to approximate by unnormalized
Gaussian distributions. This leads to an any-time
improving algorithm that can be viewed as a gener
alization of mixture-mean-field (variational algor
ithm where the approximating family is a mixture).
\nThe second part of the talk will present recent
developments on the use of variational bounds to s
olve large scale factor analysis/matrix factorizat
ion problems when data are heterogeneous (i.e. whe
n there are both discrete and continuous observati
ons) and heteroscedastic (i.e. when the data varia
nce is not the same for all the observed entities)
.\n \nCollaborators involved in these works includ
e Onno Zoeter\, Matthias Seeger\, Cedric Archambea
u\, Balaji Lakshminarayanan\, Emtiyaz Khan\, Ben M
arlin and Kevin Murphy.\n
LOCATION:Engineering Department\, CBL Room 438
CONTACT:Zoubin Ghahramani
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