[Colloquium] TTIC Talks: Brian Kulis, OSU

[Liv Leader]

REMINDER:

When:     Thursday, March 8 @ 1 p.m.

Where:    TTIC, 6045 S. Kenwood Avenue, Room #526

Who:      Brian Kulis, OSU

Title:       Revisiting k-means via Bayesian Nonparametrics

Bayesian models offer great flexibility for clustering
applications—Bayesian nonparametrics can be used for modeling infinite
mixtures, and hierarchical Bayesian models can be utilized for sharing
clusters across multiple data sets. For the most part, such
flexibility is lacking in classical clustering methods such as
k-means. Inspired by the asymptotic connection between k-means and
mixtures of Gaussians, this talk will discuss a class of algorithms
based on asymptotic generating functions that feature the benefits of
Bayesian nonparametric models but also the simplicity and scalability
of classical clustering methods. In particular, I will discuss two
novel algorithms in detail: i) a k-means-like algorithm that does not
fix the number of clusters, based on the Dirichlet process, and ii) a
scalable topic modeling algorithm, based on the hierarchical Dirichlet
process. I will discuss further extensions as well as some of our
recent empirical results.

Host: Raquel Urtasun: rurtasun at ttic.edu

-- 
Liv Leader
Human Resources Coordinator

Toyota Technological Institute Chicago
6045 S Kenwood Ave
Chicago, IL 60637
Phone- (773) 702-5033
Fax-     (773) 834-9881
Email-  lleader at ttic.edu <jam at ttic.edu>
Web-   www.ttic.edu
<http://www.ttic.edu/>
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