IPBoost – Non-Convex Boosting via Integer Programming

Marc Pfetsch; Sebastian Pokutta

ICML 2020poster8 citations

IPBoost – Non-Convex Boosting via Integer Programming

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.

BibTeX

@InProceedings{pmlr-v119-pfetsch20a,
  title = 	 {{IPB}oost {–} Non-Convex Boosting via Integer Programming},
  author =       {Pfetsch, Marc and Pokutta, Sebastian},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {7663--7672},
  year = 	 {2020},
  editor = 	 {III, Hal Daumé and Singh, Aarti},
  volume = 	 {119},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--18 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v119/pfetsch20a/pfetsch20a.pdf},
  url = 	 {https://proceedings.mlr.press/v119/pfetsch20a.html},
  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.}
}