On the problem of on-line learning with log-loss. - Yaniv Fogel; Meir Feder (ISIT, 2017)

Universal Batch Learning with Log-Loss. - Yaniv Fogel; Meir Feder (ISIT, 2018)

Universal Supervised Learning for Individual Data. - Yaniv Fogel; Meir Feder (CoRR, 2018)

A New Look at an Old Problem: A Universal Learning Approach to Linear Regression. - Koby Bibas; Yaniv Fogel; Meir Feder (ISIT, 2019)

Universal Learning of Individual Data. - Yaniv Fogel; Meir Feder (ISIT, 2019)

Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks. - Koby Bibas; Yaniv Fogel; Meir Feder (CoRR, 2019)

Efficient Data-Dependent Learnability. - Yaniv Fogel; Tal Shapira; Meir Feder (CoRR, 2020)

Another Look at Universal Individual Learning. - Yaniv Fogel; Meir Feder (ISIT, 2022)

On Multiple and Hierarchical Universality. - Yaniv Fogel; Meir Feder (ISIT, 2022)

Permutation Invariant Individual Batch Learning. - Yaniv Fogel; Meir Feder (ITW, 2023)

Combining Batch and Online Prediction. - Yaniv Fogel; Meir Feder (ISIT (Workshops), 2024)

The Universality Lens: Why Even Highly Over-Parametrized Models Learn Well. - Meir Feder; Ruediger Urbanke; Yaniv Fogel (CoRR, 2025)