Separating Models of Learning from Correlated and Uncorrelated Data
Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andrew Wan; 8(10):277−290, 2007.
We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possible examples. A recent paper by Bshouty et al. (2003) shows that the class of polynomial-size DNF formulas is efficiently learnable in this random walk model; this result suggests that the Random Walk model is more powerful than comparable standard models of learning from independent examples, in which similarly efficient DNF learning algorithms are not known. We give strong evidence that the Random Walk model is indeed more powerful than the standard model, by showing that if any cryptographic one-way function exists (a universally held belief in cryptography), then there is a class of functions that can be learned efficiently in the Random Walk setting but not in the standard setting where all examples are independent.
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