A General Theoretical Framework for Learning Smallest Interpretable Models

Ordyniak, S. orcid.org/0000-0003-1935-651X, Paesani, G., Rychlicki, M. et al. (1 more author) (2024) A General Theoretical Framework for Learning Smallest Interpretable Models. In: Wooldridge, M., Dy, J. and Natarajan, S., (eds.) Proceedings of the 38th AAAI Conference on Artificial Intelligence. Thirty-Eighth AAAI Conference on Artificial Intelligence, 20-27 Feb 2024, Vancouver, Canada. AAAI Press , Washington, DC, USA , pp. 10662-10669. ISBN 978-1-57735-887-9

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Wooldridge, M.
  • Dy, J.
  • Natarajan, S.
Copyright, Publisher and Additional Information:

© 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). This is an author produced version of a conference paper published in Proceedings of the AAAI Conference on Artificial Intelligence. Uploaded in accordance with the publisher's self-archiving policy.

Keywords: KRR: Computational Complexity of Reasoning, ML: Transparent, Interpretable, Explainable ML
Dates:
  • Published: 24 March 2024
  • Published (online): 24 March 2024
  • Accepted: 9 December 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Funding Information:
Funder
Grant number
EPSRC (Engineering and Physical Sciences Research Council)
EP/V00252X/1
Depositing User: Symplectic Publications
Date Deposited: 19 Jan 2024 14:05
Last Modified: 10 May 2024 14:30
Status: Published
Publisher: AAAI Press
Identification Number: 10.1609/aaai.v38i9.28937
Open Archives Initiative ID (OAI ID):

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