• Corpus ID: 244714780

# On computable learning of continuous features

@inproceedings{Ackerman2021OnCL,
title={On computable learning of continuous features},
author={Nathanael Leedom Ackerman and Julian Asilis and Jieqi Di and Cameron E. Freer and Jean-Baptiste Tristan},
year={2021}
}
• Published 24 November 2021
• Computer Science, Mathematics
We introduce definitions of computable PAC learning for binary classification over computable metric spaces. We provide sufficient conditions for learners that are empirical risk minimizers (ERM) to be computable, and bound the strong Weihrauch degree of an ERM learner under more general conditions. We also give a presentation of a hypothesis class that does not admit any proper computable PAC learner with computable sample function, despite the underlying class being PAC learnable.
1 Citations
On characterizations of learnability with computable learners
A characterization of a closely related notion of strong CPAC learning is given, a negative answer to the open problem posed by Agarwal et al. (2021) whether all decidable PAC learnable classes are improperly CPAC learnable, and a study of the arithmetical complexity of learnability.

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