Regularization Hyperparameters

Regularization hyperparameters control the balance between model complexity and fitting accuracy in machine learning models, aiming to improve generalization and prevent overfitting. Current research focuses on developing efficient and accurate methods for selecting these hyperparameters, exploring techniques like Bayesian optimization, gradient-based approaches, and adaptive strategies that avoid computationally expensive cross-validation. This is crucial for various applications, including image registration, regression analysis, and robust regression, as optimal hyperparameter selection significantly impacts model performance and fairness across different classes or data characteristics. The development of more sophisticated hyperparameter tuning methods is essential for building reliable and high-performing machine learning models.