Generalization Theory

Generalization theory in machine learning seeks to understand why and when models trained on a finite dataset perform well on unseen data. Current research focuses on developing tighter generalization bounds using information-theoretic approaches, analyzing the dynamics of specific training algorithms like stochastic gradient descent (SGD) and direct preference optimization (DPO), and exploring the role of model architecture in generalization, particularly for large language models and kernel regimes. These advancements aim to provide a more robust theoretical foundation for understanding the success of modern machine learning, leading to improved model design and more reliable predictions in various applications.