Agnostic learning over arbitrary distributions
If no class is too simple, then here are some agnostically PAC learnable classes.  In response to the comments, the classes with polynomially many hypotheses are crossed out:


constant depth decision trees (and other classes having only poly many hypotheses)
hyperplanes in $R^2$ (only $O(n^2)$ hypotheses producing distinct labelings)
unions of intervals (dynamic programming)
parity on some of the first $\log(k)\log\log(k)$ of $n$ bits (see this and this)
other hypothesis classes in low dimensional settings.


Pretty much everything else is NP-Hard to (at least properly) agnostically PAC learn.

Adam Kalai's tutorial on agnostic learning may also interest you.