# Semi-supervised learning with max-margin graph cuts

arXiv: https://arxiv.org/abs/2604.26818 · HTML: https://mlsanity.com/paper/2604.26818/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 13 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Built on by (most cited first, 3 of 3)

- [Online semi-supervised perception: Real-time learning without explicit feedback](https://mlsanity.com/paper/2604.27562/) (2026)
- [Learning from a single labeled face and a stream of unlabeled data](https://mlsanity.com/paper/2604.27564/) (2026)
- [Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning](https://mlsanity.com/paper/2605.03495/) (2026)

## Abstract (arXiv)

This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it to existing work, and prove a bound on its generalization error. The quality of our solutions is evaluated on a synthetic problem and three UCI ML repository datasets. In most cases, we outperform manifold regularization of support vector machines, which is a state-of-the-art approach to semi-supervised max-margin learning.