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Pointless learning (long version)

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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https://hal.archives-ouvertes.fr/hal-01429663
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Submitted on : Monday, January 23, 2017 - 6:36:34 PM
Last modification on : Monday, November 29, 2021 - 9:24:03 PM
Long-term archiving on: : Monday, April 24, 2017 - 3:23:32 PM

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  • HAL Id : hal-01429663, version 2

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

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