On characterizations of learnability with computable learners

• URL: http://arxiv.org/abs/2202.05041v1
• Date: Thu, 10 Feb 2022 13:57:20 GMT
• Title: On characterizations of learnability with computable learners
• Authors: Tom F. Sterkenburg
• Abstract summary: We study computable PAC (CPAC) learning as introduced by Agarwal et al. We give a characterization of a closely related notion of strong CPAC learning. We consider undecidability of (computable) PAC learnability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study computable PAC (CPAC) learning as introduced by Agarwal et al. (2020). First, we consider the main open question of finding characterizations of proper and improper CPAC learning. We give a characterization of a closely related notion of strong CPAC learning, and we provide a negative answer to the open problem posed by Agarwal et al. (2021) whether all decidable PAC learnable classes are improperly CPAC learnable. Second, we consider undecidability of (computable) PAC learnability. We give a simple and general argument to exhibit such undecidability, and we initiate a study of the arithmetical complexity of learnability. We briefly discuss the relation to the undecidability result of Ben-David et al. (2019), that motivated the work of Agarwal et al.

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