Hamze Bajgiran, Hamed and Echenique, Federico (2022) Closure Operators: Complexity and Applications to Classification and Decision-making. In: Proceedings of the 23rd ACM Conference on Economics and Computation. Association for Computing Machinery , New York, NY, pp. 35-55. ISBN 978-1-4503-9150-4. https://resolver.caltech.edu/CaltechAUTHORS:20220707-170604478
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Abstract
We study the complexity of closure operators, with applications to machine learning and decision theory. In machine learning, closure operators emerge naturally in data classification and clustering. In decision theory, they can model equivalence of choice menus, and therefore situations with a preference for flexibility. Our contribution is to formulate a notion of complexity of closure operators, which translate into the complexity of a classifier in ML, or of a utility function in decision theory.
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| Additional Information: | © 2022 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. The first author gratefully acknowledges support from Beyond Limits (Learning Optimal Models) through CAST (The Caltech Center for Autonomous Systems and Technologies). The second author gratefully acknowledges support from the National Science Foundation under grant number SES-1558757. We thank Chris Chambers for many comments and suggestions. We would like to thank our reviewers for their constructive feedback. | |||||||||
| Group: | Center for Autonomous Systems and Technologies (CAST) | |||||||||
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| Subject Keywords: | classification, menu choice, subjective state space | |||||||||
| DOI: | 10.1145/3490486.3538253 | |||||||||
| Record Number: | CaltechAUTHORS:20220707-170604478 | |||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20220707-170604478 | |||||||||
| Official Citation: | Hamed Hamze Bajgiran and Federico Echenique. 2022. Closure Operators: Complexity and Applications to Classification and Decision-making.. In Proceedings of the 23rd ACM Conference on Economics and Computation (EC ’22), July 11–15, 2022, Boulder, CO, USA. ACM, New York, NY, USA, 21 pages. https://doi.org/10.1145/ 3490486.3538253 | |||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
| ID Code: | 115375 | |||||||||
| Collection: | CaltechAUTHORS | |||||||||
| Deposited By: | George Porter | |||||||||
| Deposited On: | 08 Jul 2022 22:35 | |||||||||
| Last Modified: | 29 Jul 2022 18:46 |
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