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The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise

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 نشر من قبل Pasin Manurangsi
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
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We study the computational complexity of adversarially robust proper learning of halfspaces in the distribution-independent agnostic PAC model, with a focus on $L_p$ perturbations. We give a computationally efficient learning algorithm and a nearly matching computational hardness result for this problem. An interesting implication of our findings is that the $L_{infty}$ perturbations case is provably computationally harder than the case $2 leq p < infty$.



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