# Mind the Nuisance: Gaussian Process Classification using Privileged Noise

arXiv: https://arxiv.org/abs/1407.0179 · HTML: https://mlsanity.com/paper/1407.0179/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2014.

## Built on by (most cited first, 7 of 7)

- [Correlated Input-Dependent Label Noise in Large-Scale Image Classification](https://mlsanity.com/paper/2105.10305/) (2021)
- [Transfer and Marginalize: Explaining Away Label Noise with Privileged Information](https://mlsanity.com/paper/2202.09244/) (2022)
- [Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration](https://mlsanity.com/paper/2210.05126/) (2022)
- [Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy Labels](https://mlsanity.com/paper/2310.06600/) (2023)
- [Meta-learning using privileged information for dynamics](https://mlsanity.com/paper/2104.14290/) (2021)
- [Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach](https://mlsanity.com/paper/2605.07166/) (2026)
- [Quantifying the Value of Privileged Information Using a PAC-Bayesian Approach](https://mlsanity.com/paper/2609.12891/) (2026)

## Abstract (arXiv)

The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowledge into the training process of a classifier, even when this comes in the form of a data modality that is not available at test time. Here, we show that privileged information can naturally be treated as noise in the latent function of a Gaussian Process classifier (GPC). That is, in contrast to the standard GPC setting, the latent function is not just a nuisance but a feature: it becomes a natural measure of confidence about the training data by modulating the slope of the GPC sigmoid likelihood function. Extensive experiments on public datasets show that the proposed GPC method using privileged noise, called GPC+, improves over a standard GPC without privileged knowledge, and also over the current state-of-the-art SVM-based method, SVM+. Moreover, we show that advanced neural networks and deep learning methods can be compressed as privileged information.