# Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability

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Abstract:

In this paper we revisit some classic problems on classification under misspecification. In particular, we study the problem of learning halfspaces under Massart noise with rate $\eta$. In a recent work, Diakonikolas, Goulekakis, and Tzamos resolved a long-standing problem by giving the first efficient algorithm for learning to accuracy...More

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