IPBoost – Non-Convex Boosting via Integer Programming

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Authors

Marc Pfetsch, Sebastian Pokutta

Abstract

Recently non-convex optimization approaches for solving machine learning problems have gained significant attention. In this paper we explore non-convex boosting in classification by means of integer programming and demonstrate real-world practicability of the approach while circumvent- ing shortcomings of convex boosting approaches. We report results that are comparable to or better than the current state-of-the-art.