A private halfspace learner achieves empirical and population zero-one error O(1/(γ²εn) + |S_out|/(γn)) for the best separable subset S_in = S\S_out with margin γ, without knowing γ or S_out in advance.
Learning noisy halfspaces with a margin: Massart is no harder than random
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Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning
A private halfspace learner achieves empirical and population zero-one error O(1/(γ²εn) + |S_out|/(γn)) for the best separable subset S_in = S\S_out with margin γ, without knowing γ or S_out in advance.