[Ml-stat-talks] Tamara Broderick Seminar, Tuesday 5/30 at 4:30pm
Barbara Engelhardt
bee at princeton.edu
Mon May 29 22:33:34 EDT 2017
Special Seminar: Tamara Broderick (MIT)
Date: Tuesday 5/30
Time: 4:30-5:30
Location: Computer Science 104
Title: Fast Quantification of Uncertainty and Robustness with
Variational Bayes
Abstract: In Bayesian analysis, the posterior follows from the
data and a choice of a prior and a likelihood. These choices may be
somewhat subjective and reasonably vary over some range. Thus, we wish
to measure the sensitivity of posterior estimates to variation in
these choices. While the field of robust Bayes has been formed to
address this problem, its tools are not commonly used in practice. We
demonstrate that variational Bayes (VB) techniques are readily
amenable to fast robustness analysis. Since VB casts posterior
inference as an optimization problem, its methodology is built on the
ability to calculate derivatives of posterior quantities with respect
to model parameters. We use this insight to develop local prior
robustness measures for mean-field variational Bayes (MFVB), a
particularly popular form of VB due to its fast runtime on large data
sets. A potential problem with MFVB is that it has a well-known major
failing: it can severely underestimate uncertainty and provides no
information about covariance. We generalize linear response methods
from statistical physics to deliver accurate uncertainty estimates for
MFVB---both for individual variables and coherently across variables.
We call our method linear response variational Bayes (LRVB).
Speaker bio: Tamara Broderick is the ITT Career Development Assistant
Professor in the Department of Electrical Engineering and Computer Science
at MIT. She is a member of the MIT Computer Science and Artificial
Intelligence Laboratory (CSAIL), the MIT Statistics and Data Science
Center, and the Institute for Data, Systems, and Society (IDSS). She
completed her Ph.D. in Statistics with Professor Michael I. Jordan at the
University of California, Berkeley in 2014. Previously, she received an AB
in Mathematics from Princeton University (2007), a Master of Advanced Study
for completion of Part III of the Mathematical Tripos from the University
of Cambridge (2008), an MPhil by research in Physics from the University of
Cambridge (2009), and an MS in Computer Science from the University of
California, Berkeley (2013). Her recent research has focused on developing
and analyzing models for scalable Bayesian machine learning---especially
Bayesian nonparametrics. She has been awarded a Google Faculty Research
Award, the ISBA Lifetime Members Junior Researcher Award, the Savage Award
(for an outstanding doctoral dissertation in Bayesian theory and methods),
the Evelyn Fix Memorial Medal and Citation (for the Ph.D. student on the
Berkeley campus showing the greatest promise in statistical research), the
Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall
Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton
senior with the highest academic average).
--
Barbara E Engelhardt
Assistant Professor
Department of Computer Science
Center for Statistics and Machine Learning
Princeton University
http://www.cs.princeton.edu/~bee
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