N. Bshouty and E. Mossel and R. O'Donnell and R. Servedio.

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We consider a model of learning Boolean functions from examples generated by a uniform random walk on $\{0,1\}^n$. We give a polynomial time algorithm for learning decision trees and DNF formulas in this model. This is the first efficient algorithm for learning these classes in a natural passive learning model where the learner has no influence over the choice of examples used for learning.

Postscript or pdf of conference version