<html><body>Colloquium Speaker<br>Erik Sudderth, Brown University <br>Monday, November 24, 2014 - 4:30pm <br>Computer Science 105 <br><br>Flexible, Reliable, and Scalable Nonparametric Learning <br><br>Applications
of statistical machine learning increasingly involve datasets with rich
hierarchical, temporal, spatial, or relational structure. Bayesian
nonparametric models offer the promise of effective learning from big
datasets, but standard inference algorithms often fail in subtle and
hard-to-diagnose ways. We explore this issue via variants of a popular
and general model family, the hierarchical Dirichlet process. We propose
a framework for "memoized" online optimization of variational learning
objectives, which achieves computational scalability by processing local
batches of data, while simultaneously adapting the global model
structure in a coherent fashion. Using this approach, we build improved
models of text, audio, image, and social network data. <br><br>Erik B.
Sudderth is an Assistant Professor in the Brown University Department of
Computer Science. He received the Bachelor's degree (summa cum laude,
1999) in Electrical Engineering from the University of California, San
Diego, and the Master's and Ph.D. degrees (2006) in EECS from the
Massachusetts Institute of Technology. His research interests include
probabilistic graphical models; nonparametric Bayesian methods; and
applications of statistical machine learning in computer vision and the
sciences. He received an NSF CAREER award in 2014, and in 2008 was named
one of "AI's 10 to Watch" by IEEE Intelligent Systems Magazine. <br></body></html>