PAC Bayesian Bound

PAC-Bayesian bounds provide probabilistic generalization guarantees for machine learning algorithms, aiming to quantify the difference between a model's training and test performance. Current research focuses on tightening these bounds through various techniques, including leveraging information theory, Wasserstein distances, and coin-betting approaches, and applying them to diverse models such as Bayesian neural networks, graph neural networks, and variational autoencoders. This work is significant because it offers a theoretically grounded framework for understanding and improving the generalization capabilities of machine learning models, leading to more reliable and efficient algorithms.