Review for NeurIPS paper: Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability

This paper makes a solid contribution to the literature on efficiently learning halfspaces with noise, extending a paper from last year on learning with Massart noise by proposing a simpler and proper learning algorithm. It also generalizes beyond Massart noise to some extent, and establishes a lower bound for SQ learning. The reviewers are unanimous in their praise of the paper.