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arXiv · 2306.07892

Robustly Learning a Single Neuron via Sharpness

Puqian Wang·Nikos Zarifis·Ilias Diakonikolas·Jelena Diakonikolas
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Abstract

We study the problem of learning a single neuron with respect to the $L_2^2$-loss in the presence of adversarial label noise. We give an efficient algorithm that, for a broad family of activations including ReLUs, approximates the optimal $L_2^2$-error within a constant factor. Our algorithm applies under much milder distributional assumptions compared to prior work. The key ingredient enabling our results is a novel connection to local error bounds from optimization theory.

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BibTeXRIS

Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas. 2023-06-13. Robustly Learning a Single Neuron via Sharpness. https://arxiv.org/abs/2306.07892

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