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cs.LG2025
Recursively Enumerably Representable Classes and Computable Versions of the Fundamental Theorem of Statistical Learning
David Kattermann, Lothar Sebastian Krapp
We study computable probably approximately correct (CPAC) learning, where learners are required to be computable functions. It had been previously observed that the Fundamental The…
cs.LG2024
Measurability in the Fundamental Theorem of Statistical Learning
Lothar Sebastian Krapp, Laura Wirth
The Fundamental Theorem of Statistical Learning states that a hypothesis space is PAC learnable if and only if its VC dimension is finite. For the agnostic model of PAC learning, t…