# IPBoost -- Non-Convex Boosting via Integer Programming

arXiv: https://arxiv.org/abs/2002.04679 · HTML: https://mlsanity.com/paper/2002.04679/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [What killed the Convex Booster ?](https://mlsanity.com/paper/2205.09628/) (2022)
- [How to Boost Any Loss Function](https://mlsanity.com/paper/2407.02279/) (2024)

## Abstract (arXiv)

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 circumventing shortcomings of convex boosting approaches. We report results that are comparable to or better than the current state-of-the-art.