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Linear methods for large regressions -NYU Bioinformatics Data Science Talks

NYU Bioinformatics Lab (Data Science Talks)

Friday, March 15, 2013 from 6:00 PM to 7:30 PM (EDT)

Linear methods for large regressions -NYU...

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NYU Bioinformatics Data Science Talks

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Linear Methods for Large Regressions


Dean Foster

Speaker: Dean Foster

Professor of Statistics, Wharton at the University of Pennsylvania

 

Using random matrix theory, we now have some very easy to understand and fast to use methods of computing low rank representations of matrices. I have been using these methods as a hammer to improve several statistical methods. I'll discuss three of these in this talk. First, I'll show how these ideas can be used to speed up stepwise regression. Then I'll turn to using them to contruct new linear features motivated by CCA's.  Finally, I'll use these methods to get a fast way of estimating an HMM.

 

About the Speaker:

 

Dean Foster is a Professor of Statistics at the Wharton School. His primary areas of research are machine learning, NLP and game theory.  He is currently visiting MSR in NYC.

 

In game theory he has pioneered two new areas of research: social learning (which was the topic of the International Game Theory conference in 2000) and calibrated learning (which was the the topic of a special issue of {\it Games and Economic Behavior}). He has developed a method of legal contracts for the internet. Recently he was asked by the the ACLU of California for a declaration on a paper he wrote on Affirmative Action.

 

His research in statistics has been primarily directed at data mining. He was one of the pioneers of converting conventional regression techniques to the problems of big data. He has created algorithms that will safely sift through millions of features to find the few that are beneficial. He has proven these techniques are optimal in several different senses.

 

In statistics he has also done work on measuring the risk in financial markets, making good predictions in the context of data mining, statistical models of learning theory, and learning the strategic choices made by an adversary. He has looked at how human decision makers create and use confidence intervals.

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When & Where


NYU Bioinformatics Lab
715 Broadway
New York, NY 10003

Friday, March 15, 2013 from 6:00 PM to 7:30 PM (EDT)


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NYU Bioinformatics Lab (Data Science Talks)

The NYU Bioinformatics Lab has recently started running an informal series of talks on data science, machine learning, artificial intelligence, and technology enterprise. Check out our website for more details on our schedule!

Please e-mail thomson.nguyen@cs.nyu.edu if you're interested in sponsorship opportunities or speaking!

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