Iterative Regularization

Iterative regularization is a technique enhancing the performance of various machine learning and signal processing algorithms by iteratively refining solutions and incorporating regularization terms. Current research focuses on applying this approach to diverse problems, including density ratio estimation, sparse recovery (using novel norms like the k-support norm), and federated learning (with dynamic pruning and incremental regularization). This methodology improves accuracy, stability, and efficiency in tasks such as image reconstruction, classification, and deep neural network training, offering significant advantages over traditional methods in terms of speed and performance.