Generalization Bound

Generalization bounds in machine learning aim to quantify a model's ability to perform well on unseen data, based on its performance on training data. Current research focuses on developing tighter bounds for various architectures, including neural networks (especially deep and "nearly-linear" networks), large language models, and graph neural networks, often employing techniques like sample compression, PAC-Bayesian analysis, and information-theoretic approaches. These advancements are crucial for understanding and improving the reliability and robustness of machine learning models, particularly in high-stakes applications where generalization is paramount. The development of practically computable and informative bounds remains a significant challenge and active area of investigation.