Information Theoretic Generalization

Information-theoretic generalization aims to understand and bound the generalization error of machine learning models using information-theoretic quantities, such as mutual information and Kullback-Leibler divergence. Current research focuses on deriving tighter generalization bounds for various algorithms, including stochastic gradient Langevin dynamics (SGLD) and federated learning, and across diverse settings like adversarial attacks and noisy channels. This approach offers a principled way to analyze model performance and robustness, potentially leading to improved algorithm design and more reliable predictions in various applications, including quantum machine learning and edge computing. The development of tighter bounds and their application to different model architectures is a key area of ongoing investigation.