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

The paper gives a PAC learning algorithm for the basic problem of halfspaces in a model of learning with noise. The algorithm uses ideas from previous related results in the simpler model of random classification noise, with important new ideas. Learning with noise is a basic topic in learning theory. It can be argued that the most studied models (random misclassification noise and malicious noise) are unrealistically benign (even though the related SQ model is very important) or malicious, and there is a great need for the study of more realistic models. The Massart noise model is a candidate for such a model.