NIPS2014_5373.pdf (774.06 kB)
Mind the nuisance: Gaussian process classification using privileged noise
conference contribution
posted on 2023-06-08, 21:11 authored by Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H Lampert, Novi QuadriantoNovi QuadriantoThe 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 probit 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.
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Publication status
- Published
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- Published version
Journal
Proceedings of Advances in Neural Information Processing Systems 27 (NIPS 2014); Palais des Congrès de Montréal, Montréal Canada; 8 - 13 December 2014Publisher
Neural Information Processing Systems FoundationPublisher URL
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837-845Book title
Advances in Neural Information Processing Systems 27Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2015-06-18First Open Access (FOA) Date
2015-06-18First Compliant Deposit (FCD) Date
2015-06-18Usage metrics
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