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Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation by Galy-Fajou, Théo; Wenzel, Florian; Donner, Christian; Opper, Manfred is a scholarly article available to read on EtoBox.

What is Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation about?

We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function. The new likelihood has two benefits: it leads to well-calibrated uncertainty estimates and allows for an efficient latent variable augmentation. The augmented model has the advantage that it is conditionally conjugate leading to a fast variational inference method via block coordinate ascent updates. Previous approaches suffered from a trade-off between uncertainty calibration and speed. Our experiments show that our method leads to well-calibrated uncertainty estimates and competitive predictive performance while being up to two orders faster than the state of the art.

Author

Galy-Fajou, Théo; Wenzel, Florian; Donner, Christian; Opper, Manfred

Published

2019

Language

EN