Submodular functions are noise stable

Abstract

We show that all non-negative submodular functions have high noise-stability. As a consequence, we obtain a polynomial-time learning algorithm for this class with respect to any product distribution on {−1, 1} (for any constant accuracy parameter ). Our algorithm also succeeds in the agnostic setting. Previous work on learning submodular functions required… (More)

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