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Affine Natural Proximal Learning
Published Web Location
https://link.springer.com/chapter/10.1007/978-3-030-26980-7_73No data is associated with this publication.
Abstract
We revisit the natural gradient method for learning in statistical manifolds. We consider the proximal formulation and obtain a closed form approximation of the proximity term over an affine subspace of functions in the Legendre dual formulation. We consider two important types of statistical metrics, namely the Wasserstein and Fisher-Rao metrics, and introduce numerical methods for high dimensional parameter spaces.
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