Generalized Ridge Regression

Generalized ridge regression extends standard ridge regression by incorporating a matrix-valued hyperparameter, allowing for more flexible regularization and improved performance in various settings. Current research focuses on optimizing this hyperparameter, particularly within continual learning and meta-learning frameworks, aiming to leverage relationships between tasks to enhance generalization. This approach shows promise in improving model performance across diverse applications, including high-dimensional data analysis and multi-agent systems, by effectively balancing the trade-off between exploiting existing knowledge and adapting to new information. The development of efficient algorithms for hyperparameter estimation and theoretical analyses of the method's optimality are key areas of ongoing investigation.