Geometric aspects of learning from labeled and unlabeled data

SpeakerDr. Misha Beylkin
Organization Computer Science Dept., Ohio State University
LocationEBII 1230
Start Date March 16, 2007 1:00 PM
End Date March 16, 2007 2:00 PM

While inference from labeled data is one of the traditional problems of
machine learning and statistics, it is only recently that we have
developed an understanding of how unlabeled data may be helpful in
various inferential problems. It turns out that many aspects of the
connection between labeled and unlabeled data can be interpreted
geometrically.

In this talk I will discuss certain geometric invariants, centered
around the notions of the Laplace operator and the heat equation, and
their role in machine learning. I will also discuss theoretical results
on reconstructing these objects from sampled data, as well as some
recent applications of these ideas to computing volumes of convex bodies
in polynomial time.

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