Information-theoretic and PAC-Bayesian perspectives provide insights into generalization bounds in machine learning.
This paper introduces a novel framework for deriving generalization bounds for learning algorithms trained on data with dependencies encoded by a graph structure, where the strength of dependencies decays with graph distance.
This paper introduces a novel framework called "online-to-PAC conversion" that leverages online learning techniques, specifically regret analysis, to derive generalization bounds for statistical learning algorithms.