Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version
This work addresses scalability issues for GPs in safety-critical dynamical systems requiring real-time adaptation, representing an incremental improvement over existing methods.
The paper tackles the computational and memory limitations of exact Gaussian Processes (GPs) in streaming data scenarios by proposing SkyGP, a framework that maintains a bounded set of experts to reduce complexity while inheriting performance guarantees from exact GPs. It demonstrates superior performance in benchmarks and real-time control experiments compared to state-of-the-art methods.
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.