IPBoost – Non-Convex Boosting via Integer Programming
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7663-7672, 2020.
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.