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Symmetry in Statistical Models

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If you have a question about this talk, please contact Yingzhen Li.

We will give a general introduction to the use of group theory in statistics and machine learning. Then, we will focus on learning with kernels over the symmetric group of permutations in particular. Finally, we will briefly discuss how the symmetric group is also useful for constructing generative models of exchangeable graphs.

Reading:

There is no required reading, although Sections 1.1 and 5.2 of Risi Kondor’s thesis (http://people.cs.uchicago.edu/~risi/papers/KondorThesis.pdf) will be very relevant to the topics presented in the talk.

This talk is part of the Machine Learning Reading Group @ CUED series.

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