杜克大学john来我实验室交流学习

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. 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.