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The learning with privileged information setting has recently attracted a lotof attention within the machine learning community, as it allows theintegration 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 attest time. Here, we show that privileged information can naturally be treatedas noise in the latent function of a Gaussian Process classifier (GPC). Thatis, in contrast to the standard GPC setting, the latent function is not just anuisance but a feature: it becomes a natural measure of confidence about thetraining data by modulating the slope of the GPC sigmoid likelihood function.Extensive experiments on public datasets show that the proposed GPC methodusing privileged noise, called GPC+, improves over a standard GPC withoutprivileged knowledge, and also over the current state-of-the-art SVM-basedmethod, SVM+. Moreover, we show that advanced neural networks and deep learningmethods can be compressed as privileged information.

Mind the Nuisance: Gaussian Process Classification using Privileged Noise