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arXiv · 2007.15220

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

Ilias Diakonikolas·Daniel M. Kane·Pasin Manurangsi
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Abstract

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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BibTeXRIS

Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi. 2020-07-30. The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. https://arxiv.org/abs/2007.15220

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