Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version

TL;DR

This paper introduces SkyGP, a scalable streaming Gaussian process framework for online learning and control, balancing accuracy and efficiency while maintaining theoretical guarantees.

Contribution

The paper proposes a novel streaming kernel-induced expert framework for Gaussian processes that addresses computational and memory challenges in real-time online learning.

Findings

SkyGP outperforms state-of-the-art methods in benchmarks.

SkyGP variants effectively balance accuracy and computational efficiency.

Real-time control experiments validate SkyGP's practical effectiveness.

Abstract

Gaussian Processes (GPs), as a nonparametric learning method, offer flexible modeling capabilities and calibrated uncertainty quantification for function approximations. Additionally, GPs support online learning by efficiently incorporating new data with polynomial-time computation, making them well-suited for safety-critical dynamical systems that require rapid adaptation. However, the inference and online updates of exact GPs, when processing streaming data, incur cubic computation time and quadratic storage memory complexity, limiting their scalability to large datasets in real-time settings. In this paper, we propose a streaming kernel-induced progressively generated expert framework of Gaussian processes (SkyGP) that addresses both computational and memory constraints by maintaining a bounded set of experts, while inheriting the learning performance guarantees from exact Gaussian processes. Furthermore, two SkyGP variants are introduced, each tailored to a specific objective, either maximizing prediction accuracy (SkyGP-Dense) or improving computational efficiency (SkyGP-Fast). The effectiveness of SkyGP is validated through extensive benchmarks and real-time control experiments demonstrating its superior performance compared to state-of-the-art approaches.