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Statistical learning theory and Occam's razor: The argument from empirical risk minimization
This paper considers the epistemic justification for a simplicity preference in inductive inference that may be obtained from the machine learning framework of statistical learning theory.
On characterizations of learnability with computable learners
We study computable PAC (CPAC) learning as introduced by Agarwal et al. (2020).
The no-free-lunch theorems of supervised learning
The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification.
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