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Pointless Learning

Clerc, F; Danos, V; Dahlqvist, F; Garnier, I; (2017) Pointless Learning. In: Esparza, J and Murawski, AS, (eds.) Foundations of Software Science and Computation Structures. (pp. pp. 355-369). Springer Nature: Berlin, Germany. Green open access

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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.

Type: Proceedings paper
Title: Pointless Learning
Event: 20th International Conference on Foundations of Software Science and Computation Structures
Location: Uppsala, Sweden
Dates: 22nd-29th April 2017
ISBN-13: 978-3-662-54457-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-662-54458-7_21
Publisher version: https://doi.org/10.1007/978-3-662-54458-7_21
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: software engineering, formal logic, mathematical logic, formal languages, programming languages, compilers, interpreters, finite automata, coalgebra, numerical methods, Markov chains, probability, semantics, satisfiability problems, artificial intelligence, context free languages
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10064377
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