Regularization Based Method

Regularization-based methods aim to improve the performance and generalization of machine learning models by adding constraints to the optimization process, preventing overfitting and enhancing robustness. Current research focuses on developing novel regularization techniques, including adaptive and instance-optimal algorithms, and integrating them into various model architectures such as neural networks and matrix factorization methods for applications like image deblurring, deepfake detection, and continual learning. These advancements are significant because they address critical challenges in machine learning, leading to more accurate, reliable, and efficient models across diverse domains.