Double descent for least-squares interpolation on contaminated data: A simulation study

This research investigates the "double descent" phenomenon in overparametrized models, which allows for improved generalization despite classical overfitting concerns. The study specifically explores this effect in linear regression with contaminated training data, finding that significant overparametrization enables double descent even in such robust settings.