• Corpus ID: 243847809

# Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise

@inproceedings{Diakonikolas2020NearOptimalSQ,
title={Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise},
author={Ilias Diakonikolas and Daniel M. Kane},
year={2020}
}
• Published 17 December 2020
• Computer Science
We study the problem of PAC learning halfspaces with Massart noise. Given labeled samples (x, y) from a distribution D on Rd×{±1} such that the marginalDx on the examples is arbitrary and the label y of example x is generated from the target halfspace corrupted by a Massart adversary with flipping probability η(x) ≤ η ≤ 1/2, the goal is to compute a hypothesis with small misclassification error. The best known poly(d, 1/ǫ)-time algorithms for this problem achieve error of η + ǫ, which can be…
3 Citations
Optimal SQ Lower Bounds for Learning Halfspaces with Massart Noise
• Computer Science
ArXiv
• 2022
Tight statistical query lower bounds for learnining halfspaces in the presence of Massart noise are given and it is shown that for arbitrary ∈ [0, 1/2] every SQ algorithm achieving misclassification error better than requires queries of super polynomial accuracy or at least a superpolynomial number of queries.
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