PAC Bayesian Generalization Bound

PAC-Bayesian generalization bounds provide theoretical guarantees on the performance of machine learning models, focusing on bounding the difference between a model's training and test performance. Recent research extends these bounds to various model architectures, including recurrent neural networks, graph neural networks, and generative adversarial networks, and explores different loss functions, encompassing heavy-tailed distributions and adversarial settings. This work offers valuable insights into model design choices, such as parameter sharing and weight normalization, and provides a rigorous framework for analyzing the generalization capabilities of diverse learning algorithms, ultimately improving the reliability and robustness of machine learning systems.