Mind the nuisance: Gaussian process classification using privileged noise Conference Paper


Author(s): Hernandez-Lobato, Daniel; Sharmanska, Viktoriia; Kersting, Kristian; Lampert, Christoph; Quadrianto, Novi
Title: Mind the nuisance: Gaussian process classification using privileged noise
Affiliation IST Austria
Abstract: 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 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.
Keywords: Likelihood Functions; Learning systems; Artificial intelligence; Gaussian distribution; Gaussian noise (electronic); Information science; Noise pollution; Additional knowledge; Gaussian process classifications; Gaussian Processes; Information setting; Machine learning communities; SVM-based methods; Training process
Conference Title: NIPS: Neural Information Processing Systems
Conference Dates: December 8 - December 13, 2014
Conference Location: Montreal, Quebec, Canada
ISBN: 10495258
Publisher: Neural Information Processing Systems  
Date Published: 2014-12-08
URL:
Open access: yes (repository)
IST Austria Authors
  1. Christoph Lampert
    87 Lampert
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