AOS

This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives.

In the absence of explicit regularization, interpolating kernel machine has the potential to fit the training data perfectly, at the same time, still generalizes well on test data. We isolate a phenomenon of implicit regularization for minimum-norm interpolated solutions.