Agnostically Learning Juntas from Random Walks

Computer Science – Learning

Scientific paper

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12 pages

Scientific paper

We prove that the class of functions g:{-1,+1}^n -> {-1,+1} that only depend on an unknown subset of k< 0 and access to a random walk on {-1,+1}^n labeled by an arbitrary function f:{-1,+1}^n -> {-1,+1}, finds with probability at least 1-delta a k-junta that is (opt(f)+epsilon)-close to f, where opt(f) denotes the distance of a closest k-junta to f.

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