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SUMMARY:Julia Lindberg (UT Austin)
DTSTART:20230315T150000Z
DTEND:20230315T160000Z
DTSTAMP:20260423T021055Z
UID:CompAlg/9
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/CompAlg/9/">
 Estimating Gaussian mixtures using sparse polynomial moment systems</a>\nb
 y Julia Lindberg (UT Austin) as part of Machine Learning Seminar\n\n\nAbst
 ract\nThe method of moments is a statistical technique for density estimat
 ion that solves a system of moment equations to estimate the parameters of
  an unknown distribution. A fundamental question critical to understanding
  identifiability asks how many moment equations are needed to get finitely
  many solutions and how many solutions there are. We answer this question 
 for classes of Gaussian mixture models using the tools of polyhedral geome
 try. Using these results\, we present a homotopy method to perform paramet
 er recovery\, and therefore density estimation\, for high dimensional Gaus
 sian mixture models. The number of paths tracked in our method scales line
 arly in the dimension.\n
LOCATION:https://researchseminars.org/talk/CompAlg/9/
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