How abundant are good interpolators?

Perfectly fitting the training data does not guarantee good generalization. For two idealized data distributions, this talk characterizes how frequently linear interpolating classifiers attain a given generalization error. Large-deviation and concentration results show why typical interpolators can perform worse than solutions reached by gradient descent and motivate the problem of sampling them.