Unbiased Risk Estimator

Unbiased risk estimators (UREs) are statistical tools designed to accurately estimate the generalization error of machine learning models, particularly in challenging scenarios where ground truth labels are scarce or incomplete. Current research focuses on extending UREs to handle various weakly supervised learning tasks, including partial label learning, learning with augmented classes, and positive-unlabeled learning, often employing Stein's Unbiased Risk Estimator (SURE) and its variants as core components. These advancements are significant because they enable robust model training and evaluation in situations where traditional supervised learning methods are impractical, with applications ranging from medical image denoising to classification from aggregate observations. The development of generalized UREs applicable to diverse loss functions further enhances their flexibility and broad applicability.