[gov3009-l] Applied Statistics Workshop - 2/25 - Tamara Broderick

Anton Strezhnev astrezhnev at fas.harvard.edu
Mon Feb 23 10:26:51 EST 2015


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Tamara
Broderick <http://www.tamarabroderick.com/>*, a Professor of Electrical
Engineering and Computer Science at MIT.  She will be presenting work
entitled *Feature allocations, probability functions, and paintboxes.*  Please
find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/tamara-broderick-mit>
.

As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!

-- Anton

P.S.: You can now follow the workshop on Twitter at @gov3009
<https://twitter.com/gov3009>

Title: Feature allocations, probability functions, and paintboxes

Abstract: Clustering involves placing entities into mutually exclusive
categories. We wish to relax the requirement of mutual exclusivity,
allowing objects to belong simultaneously to multiple classes, a
formulation that we refer to as "feature allocation." The first step
is a theoretical one. In the case of clustering the class of
probability distributions over exchangeable partitions of a dataset
has been characterized (via exchangeable partition probability
functions and the Kingman paintbox). These characterizations support
an elegant nonparametric Bayesian framework for clustering in which
the number of clusters is not assumed to be known a priori. We
establish an analogous characterization for feature allocation; we
define notions of "exchangeable feature probability functions" and
"feature paintboxes" that lead to a Bayesian framework that does not
require the number of features to be fixed a priori. The second step
is a computational one. Rather than appealing to Markov chain Monte
Carlo for Bayesian inference, we develop a method to transform
Bayesian methods for feature allocation (and other latent structure
problems) into optimization problems with objective functions
analogous to K-means in the clustering setting. These yield
approximations to Bayesian inference that are scalable to large
inference problems.
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