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SUMMARY:Unbiased computations for MCMC-based inference of Gaussian process
covariance parameters - Maurizio Filippone (School of Computing Science\,
University of Glasgow.)
DTSTART;VALUE=DATE-TIME:20150514T141500
DTEND;VALUE=DATE-TIME:20150514T151500
UID:https://talks.ox.ac.uk/talks/id/0c7b779e-73ae-4e32-9bed-7a04dc71a36b/
DESCRIPTION:Probabilistic kernel machines based on Gaussian Processes (GPs
) are popular in several applied domains due to their flexible modelling c
apabilities and interpretability. In applications where quantification of
uncertainty is of primary interest\, it is necessary to accurately charact
erise the posterior distribution over GP covariance parameters.\n\nEmployi
ng standard inference methods would require repeatedly calculating the mar
ginal likelihood. The formidable computational challenge associated with t
his is that the marginal likelihood is only computable in the case of GP m
odels with Gaussian likelihoods applied to datasets with a limited number
of input vectors (a few thousand). For large datasets\, or for GP models w
ith non-Gaussian likelihoods\, it is not possible to compute the marginal
likelihood exactly\, and this has motivated the research community to deve
lop a variety of approximations techniques. Even though such approximation
s make it possible to recover computational tractability\, it is not possi
ble to determine to which extent they affect the characterisation of the p
osterior distribution over GP covariance parameters.\n\nIn this talk\, I w
ill present the work I carried out over the past few years in the directio
n of developing Markov chain Monte Carlo (MCMC)-based inference methods fo
r GP models that do not require the exact calculation of the marginal like
lihood\, but yield samples from the correct posterior distribution over co
variance parameters. These “noisy” MCMC methods rely only on either un
biased estimates of the marginal likelihood or stochastic gradients (unbia
sed estimates of the gradient of the logarithm of the marginal likelihood)
. I will illustrate ways of obtaining these estimates and demonstrate how
they contribute to the development of practical and scalable MCMC methods
to carry out inference of GP covariance parameters. Finally\, I will demon
strate the effectiveness of these MCMC approaches on several benchmark dat
a and on a multiple-class multiple-kernel classification problem with neur
oimaging data.\nSpeakers:\nMaurizio Filippone (School of Computing Science
\, University of Glasgow.)
LOCATION:1 South Parks Road (Lecture Theatre)\, 1 South Parks Road OX1 3TG
URL:https://talks.ox.ac.uk/talks/id/0c7b779e-73ae-4e32-9bed-7a04dc71a36b/
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DESCRIPTION:Talk:Unbiased computations for MCMC-based inference of Gaussia
n process covariance parameters - Maurizio Filippone (School of Computing
Science\, University of Glasgow.)
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