But the claim the paper is making fits perfectly with the above: > On the negative side, we prove that no algorithm can achieve accuracy of ε < 2η in learning

But the claim the paper is making fits perfectly with the above: > On the negative side, we prove that no algorithm can achieve accuracy of ε < 2η in learning any non-trivial class of functions. Though this is for a very particular type of adversary which has a lot more power than I'm claiming. In my case, though, I'm strengthening the side that the adversary has a very large number (e.g. infinite, as my claim "N->infty") of possible examples in the training set to be eps-close to. --- Rereading the above: Sorry, I'm not quite sure I understood your point! Are you claiming that there is such a model which is resilient to adversarial examples, and also has a large number of parameters relative to the hypothesis class size? (e.g. say VC dimension?) In that case, my claim would be definitely false, but I have yet to see such a paper (if you have a reference, I'd love to check it out!)