Regularization Property

Regularization, in machine learning, aims to prevent overfitting and improve model generalization by constraining the solution space. Current research focuses on understanding and enhancing implicit regularization—the inherent biases of training algorithms and architectures like stochastic gradient descent and residual networks (ResNets)—as well as developing novel explicit regularization techniques. This work spans various applications, including inverse problems (e.g., image reconstruction) and generative modeling (e.g., variational autoencoders), with a significant emphasis on theoretical analysis to provide guarantees on model performance and convergence. Improved regularization methods promise more robust and reliable machine learning models across diverse fields.