3 papers
cs.IT2025
Leave-One-Out Learning with Log-Loss
Yaniv Fogel, Meir Feder
We study batch learning with log-loss in the individual setting, where the outcome sequence is deterministic. Because empirical statistics are not directly applicable in this regim…
cs.LG2025
Information-Theoretic Framework for Understanding Modern Machine-Learning
Meir Feder, Ruediger Urbanke, Yaniv Fogel
We introduce an information-theoretic framework that views learning as universal prediction under log loss, characterized through regret bounds. Central to the framework is an effe…
cs.LG2024
Error Exponent in Agnostic PAC Learning
Adi Hendel, Meir Feder
Statistical learning theory and the Probably Approximately Correct (PAC) criterion are the common approach to mathematical learning theory. PAC is widely used to analyze learning p…