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Tamara Broderick

Wednesday 16th December 2015

Time: 4.00pm

 

Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG

 

Statistical and computational trade-offs in Bayesian learning

 

The flexibility, modularity, and coherent uncertainty estimates provided
by Bayesian posterior inference have made this approach indispensable in
a variety of domains. Since posteriors for many problems of interest
cannot be calculated exactly, much work has focused on delivering
accurate posterior approximations---though the computational cost of
these approximations can sometimes be prohibitive, particularly in a
modern, large-data context. Focusing on unsupervised learning problems,
we illustrate in a series of examples how we can trade off some typical
Bayesian desiderata for computational gains and vice versa. On one end
of the spectrum, we sacrifice learning uncertainty to deliver fast,
flexible methods for point estimates. In particular, we consider taking
limits of Bayesian posteriors to obtain novel K-means-like objective
functions as well as scalable, distributed algorithms. On the other end,
we consider mean-field variational Bayes (MFVB), a popular and fast
posterior approximation method that is known to provide poor estimates
of parameter covariance. We develop an augmentation to MFVB that
delivers accurate estimates of posterior uncertainty for model parameters.