Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
This work addresses the challenge of scalable and calibrated classification for practitioners needing efficient uncertainty quantification in large datasets, representing an incremental improvement over existing methods.
The paper tackles the computational inefficiency of Gaussian process classification in large-scale settings by proposing a novel approach that applies Gaussian process regression directly to classification labels and calibrates predictions using a Dirichlet distribution interpretation, achieving comparable accuracy and uncertainty quantification with significantly reduced computational resources.
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.