TLDR; is there any results showing that more concentrated (or easier) distributions are easier to learn?
In PAC-learning, the guarantee is given for any underlying distributions. But in reality, we don't need the guarantees for any distribution. For example for image classification, there is a limited underlying distribution. It makes sense to have stronger guarantees for easier distributions. Are there any such results?