Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective

Simeone, Osvaldo and Romano, Yaniv (2026) Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective. IEEE Journal on Selected Areas in Information Theory. (In Press)

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Item Type:	Article
Additional Information:	To appear
Disciplines:	Computing, Maths, Engineering and Natural Sciences > Computing and Information Systems
Depositing User:	Osvaldo Simeone
Date Deposited:	13 Jul 2026 15:17
Last Modified:	13 Jul 2026 15:17
URI:	https://nul.repository.guildhe.ac.uk/id/eprint/2783

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