Tighter CMI-Based Generalization Bounds via Stochastic Projection and Quantization

TL;DR

In this paper, we leverage stochastic projection and lossy compression to establish new conditional mutual information (CMI) bounds on the generalization error of statistical learning algorithms. It is shown that these bounds are generally tighter than the existing ones.

Opening excerpt from the authors’ abstract. source