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Mind the Nuisance: Gaussian Process Classification using Privileged Noise
Daniel Hernández-lobato · Viktoriia Sharmanska · Kristian Kersting · Christoph Lampert · Novi Quadrianto

Tue Dec 09 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

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.

Author Information

Daniel Hernández-lobato (Universidad Autonoma de Madrid)
Viktoriia Sharmanska (University of Sussex, Imperial College London)
Kristian Kersting (University of Bonn and Fraunhofer IAIS)
Christoph Lampert (Institute of Science and Technology Austria (ISTA))
Christoph Lampert

Christoph Lampert received the PhD degree in mathematics from the University of Bonn in 2003. In 2010 he joined the Institute of Science and Technology Austria (ISTA) first as an Assistant Professor and since 2015 as a Professor. There, he leads the research group for Machine Learning and Computer Vision, and since 2019 he is also the head of ISTA's ELLIS unit.

Novi Quadrianto (University of Sussex and HSE)

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