# Asymptotic Bayes risk of semi-supervised learning with uncertain labeling

arXiv: https://arxiv.org/abs/2403.17767 · HTML: https://mlsanity.com/paper/2403.17767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Asymptotic Bayes risk for Gaussian mixture in a semi-supervised setting](https://mlsanity.com/paper/1907.03792/) (2019)
- [A random matrix analysis and improvement of semi-supervised learning for large dimensional data](https://mlsanity.com/paper/1711.03404/) (2017)
- [Consistent Semi-Supervised Graph Regularization for High Dimensional Data](https://mlsanity.com/paper/2006.07575/) (2020)
- [Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture](https://mlsanity.com/paper/2303.02048/) (2023)
- [A Large Dimensional Analysis of Multi-task Semi-Supervised Learning](https://mlsanity.com/paper/2402.13646/) (2024)

## Abstract (arXiv)

This article considers a semi-supervised classification setting on a Gaussian mixture model, where the data is not labeled strictly as usual, but instead with uncertain labels. Our main aim is to compute the Bayes risk for this model. We compare the behavior of the Bayes risk and the best known algorithm for this model. This comparison eventually gives new insights over the algorithm.