Time-Space Hardness of Learning Sparse Parities
STOC, pp. 1067-1080, 2017.
lower boundsbounded storage cryptographybranching programFourier analysisPAC learningMore(1+)
We define a concept class F to be time-space hard (or memory-samples hard) if any learning algorithm for F requires either a memory of size super-linear in n or a number of samples super-polynomial in n, where n is the length of one sample. A recent work shows that the class of all parity functions is time-space hard [Raz, FOCSâ16]. Bui...More
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