  We study the problem of learning halfspaces with Massart noise in the
distribution-specific PAC model. We give the first computationally efficient
algorithm for this problem with respect to a broad family of distributions,
including log-concave distributions. This resolves an open question posed in a
number of prior works. Our approach is extremely simple: We identify a smooth
{\em non-convex} surrogate loss with the property that any approximate
stationary point of this loss defines a halfspace that is close to the target
halfspace. Given this structural result, we can use SGD to solve the underlying
learning problem.
