# From Cutting Planes Algorithms to Compression Schemes and Active Learning

arXiv: https://arxiv.org/abs/1508.02986 · HTML: https://mlsanity.com/paper/1508.02986/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2015.

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

- [Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes](https://mlsanity.com/paper/2410.02145/) (2024)
- [On Quantum Perceptron Learning via Quantum Search](https://mlsanity.com/paper/2503.17308/) (2025)

## Abstract (arXiv)

Cutting-plane methods are well-studied localization(and optimization) algorithms. We show that they provide a natural framework to perform machinelearning ---and not just to solve optimization problems posed by machinelearning--- in addition to their intended optimization use. In particular, theyallow one to learn sparse classifiers and provide good compression schemes.Moreover, we show that very little effort is required to turn them intoeffective active learning methods. This last property provides a generic way todesign a whole family of active learning algorithms from existing passivemethods. We present numerical simulations testifying of the relevance ofcutting-plane methods for passive and active learning tasks.