# On Generalizing the C-Bound to the Multiclass and Multi-label Settings

arXiv: https://arxiv.org/abs/1501.03001 · HTML: https://mlsanity.com/paper/1501.03001/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2015.

## Built on by (most cited first, 1 of 1)

- [A Framework for Bounding Deterministic Risk with PAC-Bayes: Applications to Majority Votes](https://mlsanity.com/paper/2510.25569/) (2025)

## Abstract (arXiv)

The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this work to more complex outputs, by providing generalizations of the C-bound to the multiclass and multi-label settings.