Conference Paper/Proceeding/Abstract 204 views
A Computability Perspective on (Verified) Machine Learning
,
Tonicha Crook,
Jay Morgan ,
Arno Pauly ,
Markus Roggenbach
Recent Trends in Algebraic Development Techniques, Volume: 13710, Pages: 63 - 80
Swansea University Authors:
Jay Morgan , Tonicha Crook, Arno Pauly , Markus Roggenbach
DOI (Published version): 10.1007/978-3-031-43345-0_3
Abstract
In Computer Science there is a strong consensus that it is highly desirable to combine the versatility of Machine Learning (ML) with the assurances formal verification can provide. However, it is unclearwhat such ‘verified ML’ should look like.This paper is the first to formalise the concepts of cla...
| Published in: | Recent Trends in Algebraic Development Techniques |
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| ISBN: | 9783031433443 9783031433450 |
| ISSN: | 0302-9743 1611-3349 |
| Published: |
Cham
Springer Nature Switzerland
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63849 |
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| Abstract: |
In Computer Science there is a strong consensus that it is highly desirable to combine the versatility of Machine Learning (ML) with the assurances formal verification can provide. However, it is unclearwhat such ‘verified ML’ should look like.This paper is the first to formalise the concepts of classifiers and learners in ML in terms of computable analysis. It provides results about which properties of classifiers and learners are computable. By doing this we establish a bridge between the continuous mathematics underpinning ML and the discrete setting of most of computer science.We define the computational tasks underlying the newly suggested verified ML in a model-agnostic way, i.e., they work for all machine learning approaches including, e.g., random forests, support vector machines, and Neural Networks. We show that they are in principle computable. |
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| Keywords: |
Machine Learning, adversarial examples, formal verification, computable analysis |
| College: |
Faculty of Science and Engineering |
| Start Page: |
63 |
| End Page: |
80 |