The paper delves into the concept of realisability in statistical learning theory under the assumption of epistemic uncertainty. The author considers a scenario where the train and test distribution are…
Continue readingThe paper delves into the epistemic justification for a simplicity preference in inductive inference, derived from the machine learning framework of statistical learning theory. It combines elements from earlier arguments…
Continue readingThe authors derive generalization bounds involving an arbitrary convex comparator function, which measures the discrepancy between training and population loss. The research is based on the assumption that the cumulant-generating…
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