Model Regularization

Model regularization techniques aim to prevent overfitting in machine learning models, improving their generalization ability and robustness. Current research focuses on developing novel regularization methods tailored to specific model architectures, such as those used in federated learning and large language models, and exploring information-theoretic approaches to guide regularization. These advancements are crucial for enhancing the performance and reliability of machine learning models across diverse applications, from healthcare to computer vision, by mitigating the challenges posed by complex data and limited training data.