Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification

TL;DR

This paper introduces a Dirichlet-based Gaussian process method for large-scale classification that achieves comparable accuracy and uncertainty quantification to traditional Gaussian process classifiers but with significantly reduced computational cost.

Contribution

The paper proposes a novel Dirichlet-based approach applied to Gaussian process regression for efficient large-scale classification with calibrated uncertainty estimates.

Findings

Achieves similar accuracy to Gaussian process classification.

Requires significantly less computational resources.

Provides well-calibrated uncertainty estimates.

Abstract

In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled approach, but the corresponding computational burden is hardly sustainable in large-scale problems and devising efficient alternatives is a challenge. In this work, we investigate if and how Gaussian process regression directly applied to the classification labels can be used to tackle this question. While in this case training time is remarkably faster, predictions need be calibrated for classification and uncertainty estimation. To this aim, we propose a novel approach based on interpreting the labels as the output of a Dirichlet distribution. Extensive experimental results show that the proposed approach provides essentially the same accuracy and uncertainty quantification of Gaussian process classification while requiring only a fraction of computational resources.