Ridge Regularization

Ridge regularization is a technique used to improve the generalization performance of machine learning models by adding a penalty to the model's complexity, preventing overfitting. Current research focuses on understanding its behavior in various settings, including out-of-distribution prediction and adversarial robustness, often within the context of kernel ridge regression and neural networks (e.g., ResNet-50). These studies aim to establish theoretical guarantees for the effectiveness of ridge regularization and develop efficient methods for tuning the regularization parameter, such as generalized cross-validation (GCV), leading to improved model accuracy and reliability in diverse applications. The insights gained are crucial for building more robust and reliable machine learning models across various domains.