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The Ethereal
Efficiently Learning Adversarially Robust Halfspaces with Noise
May 15, 2020 ยท Declared Dead ยท ๐ International Conference on Machine Learning
"No code URL or promise found in abstract"
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Authors
Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
arXiv ID
2005.07652
Category
cs.LG: Machine Learning
Cross-listed
cs.DS,
stat.ML
Citations
33
Venue
International Conference on Machine Learning
Last Checked
6 months ago
Abstract
We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. In the realizable setting, we provide necessary and sufficient conditions on the adversarial perturbation sets under which halfspaces are efficiently robustly learnable. In the presence of random label noise, we give a simple computationally efficient algorithm for this problem with respect to any $\ell_p$-perturbation.
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