**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.