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Noise-tolerant Learning, the Parity problem, and the Statistical Query model

2018

We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptographic and coding problems. Our algorithm runs in polynomial time for the case of parity functions that depend on only the first O(log n log log n) bits of input, which provides the first known instance of an efficient noise-tolerant algorithm for a concept class that is not learnable in the Statistical Query model of

doi:10.1184/r1/6607733
fatcat:6nmimctsbraqng75ny2nldbbxu