Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation

2019/05/23 by Théo Galy-Fajou, Florian Wenzel, Galy-Fajou, Théo +5 · 1 citation
Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1905.09670

openalex publication_date 2019/05/23 · openalex created_date 2019/08/13 · openalex updated_date 2026/07/28

Abstract

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.

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