[Ml-stat-talks] Daniel Hsu talk, Thurs, 3/28, 4:30pm, CS 105

Robert Schapire schapire at CS.Princeton.EDU
Wed Mar 27 12:05:09 EDT 2013

*Fast learning algorithms for discovering the hidden structure in data*
*Daniel Hsu <http://cseweb.ucsd.edu/%7Edjhsu/>*, Microsoft Research New 
England <http://research.microsoft.com/en-us/labs/newengland/>
Thursday, March 28, 2013, 4:30pm
Computer Science 105

A major challenge in machine learning is to reliably and automatically 
discover hidden structure in data with minimal human intervention. For 
instance, one may be interested in understanding the stratification of a 
population into subgroups, the thematic make-up of a collection of 
documents, or the dynamical process governing a complex time series. 
Many of the core statistical estimation problems for these applications 
are, in general, provably intractable for both computational and 
statistical reasons; and therefore progress is made by shifting the 
focus to realistic instances that rule out the intractable cases. In 
this talk, I'll describe a general computational approach for correctly 
estimating a wide class of statistical models, including Gaussian 
mixture models, Hidden Markov models, Latent Dirichlet Allocation, 
Probabilistic Context Free Grammars, and several more. The key idea is 
to exploit the structure of low-order correlations that is present in 
high-dimensional data. The scope of the new approach extends beyond the 
purview of previous algorithms; and it leads to both new theoretical 
guarantees for unsupervised machine learning, as well as fast and 
practical algorithms for large-scale data analysis.

Daniel Hsu is a postdoc at Microsoft Research New England. Previously, 
he was a postdoc with the Department of Statistics at Rutgers University 
and the Department of Statistics at the University of Pennsylvania from 
2010 to 2011, supervised by Tong Zhang and Sham M. Kakade. He received 
his Ph.D. in Computer Science in 2010 from UC San Diego, where he was 
advised by Sanjoy Dasgupta; and his B.S. in Computer Science and 
Engineering in 2004 from UC Berkeley. His research interests are in 
algorithmic statistics and machine learning.
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