Maximum information divergence from linear and toric models

  • Yulia Alexandr (University of Califorina, Berkeley)
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I will revisit the problem of maximizing information divergence from a new perspective using logarithmic Voronoi polytopes. We will see that for linear models, the maximum is always achieved at the boundary of the probability simplex. For toric models, I will describe an algorithm that combines the combinatorics of the chamber complex with numerical algebraic geometry. I will pay special attention to reducible models and models of maximum likelihood degree one, with many colorful examples. This talk is based on joint work with Serkan Hoşten.


30.05.24 13.06.24

Math Machine Learning seminar MPI MIS + UCLA

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Katharina Matschke

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