5 papers
Maximum Cut Algorithms and Upper Bounds for Planar and Toroidal Graphs
Mark Glass, Meir Feder
We demonstrate that the problem of finding the maximum cut of a planar graph with arbitrary weights can be easily mapped to a minimum T-join problem in the absolute dual graph - th…
Misspecified Universal Learning
Shlomi Vituri, Meir Feder
This paper addresses the problem of universal learning under model misspecification with log-loss. In this setting, the learner operates with a hypothesis class of models denoted b…
Implicit Binarization via Complex Phase Dynamics in Combinatorial Optimization
Khen Cohen, Mark Glass, Meir Feder +1
We introduce a physics-inspired continuous relaxation framework that yields substantially improved solutions for NP-hard combinatorial optimization problems, including Quadratic Un…
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…