Agnostic PAC sampling lower bound It is well-known that for classical PAC learning, $\Omega(d/\varepsilon)$ examples are necessary in order to acheive an error bound of $\varepsilon$ w.h.p., where $d$ is the VC-dimension of the concept class.

Is it known that $\Omega(d/\varepsilon^2)$ examples are needed in the agnostic case?
I now realize that a lower bound has indeed been established by Anthony and Bartlett (see the presentation here). 

Edit 24-Sep-2018. This question has kept me occupied all of these years, and recently, I. Pinelis and I have obtained the exact optimal constant in the agnostic PAC lower bound to appear in Ann. Stat.