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Pointless learning (long version)

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Abstract

Bayesian inversion is at the heart of probabilistic programming and more generally machine learning. Understanding inversion is made difficult by the pointful (kernel-centric) point of view usually taken in the literature. We develop a pointless (kernel-free) approach to inversion. While doing so, we revisit some foundational objects of probability theory, unravel their category-theoretical underpinnings and show how pointless Bayesian inversion sits naturally at the centre of this construction .
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Dates and versions

hal-01429663 , version 1 (09-01-2017)
hal-01429663 , version 2 (23-01-2017)

Identifiers

  • HAL Id : hal-01429663 , version 2

Cite

Florence Clerc, Vincent Danos, Fredrik Dahlqvist, Ilias Garnier. Pointless learning (long version). 2017. ⟨hal-01429663v2⟩
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