Information Theoretic Generalization Bound

Information-theoretic generalization bounds aim to quantify a machine learning model's ability to generalize to unseen data using information-theoretic measures like mutual information and Kullback-Leibler divergence. Current research focuses on tightening these bounds for deep neural networks and other complex models, often by incorporating concepts like model compressibility and employing techniques such as slicing the parameter space or leveraging optimal transport. These advancements offer more practical and computationally feasible bounds, improving our understanding of generalization and potentially informing the design of more robust and generalizable algorithms. The ultimate goal is to provide theoretically grounded insights into why and when machine learning models generalize well, bridging the gap between theory and practice.