Streaming Sparse Gaussian Process Approximations

TL;DR

This paper introduces a new principled framework for online Gaussian process models that efficiently update hyperparameters and pseudo-inputs with streaming data, overcoming limitations of previous methods.

Contribution

It develops a novel framework for streaming GPs that handles hyperparameter learning and pseudo-input optimization in an online, principled manner.

Findings

Effective hyperparameter updates in streaming scenarios

Robust pseudo-input optimization with new data

Validated on synthetic and real-world datasets

Abstract

Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution over function values and the hyperparameter estimates are updated in an online fashion. The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive. This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing methods for learning hyperparameters and optimising pseudo-input locations. The proposed framework is assessed using synthetic and real-world datasets.