Double Descent

Double descent describes the unexpected phenomenon where the generalization performance of a machine learning model initially worsens with increasing model complexity (as expected), then improves again beyond a certain point of overparameterization. Current research focuses on understanding the mechanisms driving this behavior across various model architectures, including linear regression, neural networks (both shallow and deep), and graph convolutional networks, often investigating the roles of optimization algorithms, regularization techniques, and data characteristics like noise and distribution shifts. This counter-intuitive finding challenges established learning theory and has significant implications for model selection and the design of more efficient and robust machine learning systems.

Papers