Learning DNF from Random Walks

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.