Reviews: Distribution-Independent PAC Learning of Halfspaces with Massart Noise

This is a very strong paper that makes impressive progress on the long-standing open problem of efficiently PAC learning halfspaces under the Massart noise model. While resolving the problem would involve getting within epsilon of the optimal error, achieving eta epsilon is a breakthrough and likely will fuel future results in learning theory.