How abundant are good interpolators?

Key points

It establishes a large-deviation principle for the generalization error when a classifier is chosen at random from the set of interpolating classifiers.
It shows that almost all interpolating classifiers concentrate exponentially around the same generalization performance.
The analysis is carried out on two natural distributions: Gaussian mixture models and logistic models.
In the overparameterized regime, gradient descent and linear programming outperform random interpolators.
The result suggests that benign overfitting in practical algorithms is a nontrivial phenomenon rather than something obvious.

External research summaries. These are not HDATF publications or measured product results.