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Reliably Learning the ReLU in Polynomial Time
These results are in contrast to known efficient algorithms for reliably learning linear threshold functions, where $\epsilon$ must be $\Omega(1)$ and strong assumptions are required on the marginal distribution.
Distribution-Independent Reliable Learning
The goal in the positive reliable agnostic framework is to output a hypothesis with the following properties: (i) its false positive error rate is at most $\epsilon$, (ii) its false negative error rate is at most $\epsilon$ more than that of the best positive reliable classifier from the class.
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