Implicit Regularization

Implicit regularization refers to the phenomenon where optimization algorithms, even without explicit regularization terms, implicitly favor certain solutions during training, often leading to improved generalization. Current research focuses on understanding this implicit bias in various models, including neural networks (e.g., deep linear networks, residual networks, and transformers), and algorithms (e.g., stochastic gradient descent and its variants), particularly in the context of matrix factorization, phase retrieval, and continual learning. This research is significant because it helps explain the surprising generalization ability of overparameterized models and can lead to the development of more efficient and robust machine learning algorithms for diverse applications.