Gives a poly-time testable learner for halfspaces with Gaussian marginals and Massart noise, plus a super-polynomial lower bound separating testable learning from classical learning under random classification noise.
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Testing Noise Assumptions of Learning Algorithms
Gives a poly-time testable learner for halfspaces with Gaussian marginals and Massart noise, plus a super-polynomial lower bound separating testable learning from classical learning under random classification noise.