2 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…