  Deep learning is renowned for its theory-practice gap, whereby principled
theory typically fails to provide much beneficial guidance for implementation
in practice. This has been highlighted recently by the benign overfitting
phenomenon: when neural networks become sufficiently large to interpolate the
dataset perfectly, model performance appears to improve with increasing model
size, in apparent contradiction with the well-known bias-variance tradeoff.
While such phenomena have proven challenging to theoretically study for general
models, the recently proposed Interpolating Information Criterion (IIC)
provides a valuable theoretical framework to examine performance for
overparameterized models. Using the IIC, a PAC-Bayes bound is obtained for a
general class of models, characterizing factors which influence generalization
performance in the interpolating regime. From the provided bound, we quantify
how the test error for overparameterized models achieving effectively zero
training error depends on the quality of the implicit regularization imposed by
e.g. the combination of model, optimizer, and parameter-initialization scheme;
the spectrum of the empirical neural tangent kernel; curvature of the loss
landscape; and noise present in the data.
