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CATEGORIES:Machine Learning Reading Group @ CUED
SUMMARY:Variational inference in graphical models: The vie
w from the marginal polytope - David Knowles and R
ichard Turner (University of Cambridge)
DTSTART;TZID=Europe/London:20100204T140000
DTEND;TZID=Europe/London:20100204T153000
UID:TALK22276AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/22276
DESCRIPTION:In last week's RCC\, we saw that loopy belief prop
agation could be connected to a constrained variat
ional free energy optimisation. The constraints en
sured that the beliefs normalised and that they we
re locally consistent. This week\, we'll describe
an alternate view of this optimisation which separ
ately considers the constraints (the domain being
called the marginal polytope) and the free-energy.
The optimization takes place over the lower dimen
sional space of generalised exponential family mea
n parameters. This representation clarifies that t
here are two distinct components to variational in
ference algorithms: (a) an approximation to the en
tropy function\; and (b) an approximation to the m
arginal polytope. This viewpoint clarifies the ess
ential ingredients of known variational methods\,
and also suggests novel relaxations. Taking the “z
ero-temperature limit” recovers a variational repr
esentation for MAP computation as a linear program
(LP) over the marginal polytope.\n\nThe material
we hope to cover (and probably some extra) is cove
red on slides 1-13 and 23-39 of this "tutorial":ht
tp://www.eecs.berkeley.edu/~wainwrig/icml08/Wainwr
ight_ICML08.pdf\n\nIf you feel inclined to delve i
nto the theory a little more\, refer to "this pape
r":http://www.cs.berkeley.edu/~jordan/papers/WaiJo
r_Aller03.ps\n
LOCATION:Engineering Department\, CBL Room 438
CONTACT:Shakir Mohamed
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