28 citations · 46 across the 7 of their papers we have counts for
7 papers
A Layered Simplex Architecture for Large Alphabets
Meir Feder, Yaniv Fogel, Ruediger Urbanke
A learning architecture induces a prior over its expressible models. Recent work links architectural success to a broad complexity spectrum: model probabilities spanning many order…
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…
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…
Efficient Data-Dependent Learnability
Yaniv Fogel, Tal Shapira, Meir Feder
The predictive normalized maximum likelihood (pNML) approach has recently been proposed as the min-max optimal solution to the batch learning problem where both the training set an…
A New Look at an Old Problem: A Universal Learning Approach to Linear Regression
Koby Bibas, Yaniv Fogel, Meir Feder
Linear regression is a classical paradigm in statistics. A new look at it is provided via the lens of universal learning. In applying universal learning to linear regression the hy…
Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks
Koby Bibas, Yaniv Fogel, Meir Feder
The Predictive Normalized Maximum Likelihood (pNML) scheme has been recently suggested for universal learning in the individual setting, where both the training and test samples ar…
Universal Supervised Learning for Individual Data
Yaniv Fogel, Meir Feder
Universal supervised learning is considered from an information theoretic point of view following the universal prediction approach, see Merhav and Feder (1998). We consider the st…