Cutting-plane methods have enabled remarkable successes in integer program solving over the last few decades. All state-of-the-art modern-day solvers integrate a myriad of cutting-plane techniques to speed up the underlying branch-and-bound tree-search algorithm used to find optimal solutions. In this paper we prove the first guarantees for learning high-performing cut-selection policies tailored to the instance distribution at hand using samples. We first bound the sample complexity of learning…