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Mahito Sugiyama (National Institute of Informatics, JST, PRESTO): Learning with Dually Flat Structure and Incidence Algebra

Ort: MPI für Mathematik in den Naturwissenschaften Leipzig,  , Videobroadcast

Video broadcast: Math Machine Learning seminar MPI MIS + UCLA Statistical manifolds with dually flat structures, such as an exponential family, appear in various machine learning models. In this talk, I will introduce a close connection between dually flat manifolds and incidence algebras in order theory and present its application to machine learning. This approach allows us to flexibly design log-linear models equipped with partially ordered sample spaces, which include a number of machine learning problems such as learning of Boltzmann machines, tensor decomposition, and blind source separation. I will also talk about theoretical analysis of such models using Rissanen's stochastic complexity and draw the connection to the double descent phenomenon via model volumes.

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Beginn: Sept. 17, 2020, 5 p.m.

Ende: Sept. 17, 2020, 6:30 p.m.