05/05/2015 - 12:15pm

05/05/2015 - 1:10pm

Speaker:

Lily Silverstein (CGU)

Abstract:

Some machine learning problems are naturally modeled by probability distributions (or other data) defined over a finite group. In this case the generalized Fourier transform, based on irreducible group representations, is a useful tool for designing efficient algorithms. In the case of the symmetric group, we can use combinatorial objects like Young diagrams to define an analogue to bandlimiting. Finally, I will talk about how certain probabilistic inferences can be performed directly in the Fourier domain, by considering the combinatorial decomposition of tensor products of representations. This talk is expository and based mainly on work done by Jonathan Huang and Risi Kondor.

Where:

Mudd Science Library 126, Pomona College

__Claremont Graduate University__ | __Claremont McKenna__ | __Harvey Mudd__ | __Pitzer__ | __Pomona__ | __Scripps__

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