[D] Modern applications of statistical learning theory?
I was reading about concentration of measure related stuff recently and was curious whether anyone knows whether this material is still applicable to ‘deep learning’ models. By statistical learning theory I mean stuff like VC / Rademacher bounds etc.
If it is, can anyone point to any research papers on this topic?
From my naive understanding, because these bounds relate to the worst-case scenario the union bound may be excessively pessimistic in terms of the number of training examples required.