Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Year: 2019
Type: preprint
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.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1332273124, Verbundprojekt: DeepIntegrate - Integration heterogener Datenquellen im Deep Learning: Architekturen, Algorithmen und Anwendungen in der Pflanzenzüchtung, G4428432807, i:DSem - Verbundprojekt: PREDICT - Umfassende Datenintegration zur Verbesserung onkologischer Therapien - Teilprojekt A
OpenAlex ID: W2946610432