[Ml-stat-talks] Fwd: [talks] Colloquium Speaker John Paisley Mon Feb 25, 4:30pm

David Blei blei at CS.Princeton.EDU
Tue Feb 19 16:27:25 EST 2013


all

next monday, john paisley from berkeley will speak about scalable
bayesian nonparametrics.  this will be interesting to those of you who
like graphical models, bayesian nonparametrics, scalable bayesian
computation, or topic models.  (i like all of the above.)

best
dave



---------- Forwarded message ----------
From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
Date: Tue, Feb 19, 2013 at 1:43 PM
Subject: [talks] Colloquium Speaker John Paisley Mon Feb 25, 4:30pm
To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>


Bayesian Nonparametric Models and "Big Data"
John Paisley, University of California, Berkeley
Monday, February 25, 2013, 4:30pm
Computer Science Small Auditorium, Room 105

Bayesian nonparametrics is an area in machine learning in which models
grow in size and complexity as data accrue. As such, they they are
particularly relevant to the world of "Big Data", where it may be
difficult or even counterproductive to fix the number of parameters a
priori. A stumbling block for Bayesian nonparametrics has been that
their algorithms for posterior inference generally show poor
scalability. In this talk, we tackle this issue in the domain of
large-scale text collections. Our model is a novel tree-structured
model in which documents are represented by collections of paths in an
infinite-dimensional tree. We develop a general and efficient
variational inference strategy for learning such models based on
stochastic optimization, and show that with this combination of
modeling and inference approach, we are able to learn high-quality
models using millions of documents.

John Paisley received the B.S.E. (2004), M.S. (2007) and Ph.D. (2010)
in Electrical & Computer Engineering from Duke University, where his
advisor was Lawrence Carin. He was a postdoctoral researcher with
David Blei in the Computer Science Department at Princeton University,
and currently with Michael Jordan in the Department of EECS at UC
Berkeley. He works on developing Bayesian models for machine learning
applications, particularly for dictionary learning and topic modeling.

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