Conditioning Sparse Variational Gaussian Processes for Online Decision-making

• URL: http://arxiv.org/abs/2110.15172v1
• Date: Thu, 28 Oct 2021 14:49:34 GMT
• Title: Conditioning Sparse Variational Gaussian Processes for Online Decision-making
• Authors: Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
• Abstract summary: Online variational conditioning (OVC) is a procedure for efficiently conditioning SVGPs in an online setting. OVC provides compelling performance in a range of applications including active learning of malaria incidence.
• Score: 40.13509498161087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With a principled representation of uncertainty and closed form posterior updates, Gaussian processes (GPs) are a natural choice for online decision making. However, Gaussian processes typically require at least $\mathcal{O}(n^2)$ computations for $n$ training points, limiting their general applicability. Stochastic variational Gaussian processes (SVGPs) can provide scalable inference for a dataset of fixed size, but are difficult to efficiently condition on new data. We propose online variational conditioning (OVC), a procedure for efficiently conditioning SVGPs in an online setting that does not require re-training through the evidence lower bound with the addition of new data. OVC enables the pairing of SVGPs with advanced look-ahead acquisition functions for black-box optimization, even with non-Gaussian likelihoods. We show OVC provides compelling performance in a range of applications including active learning of malaria incidence, and reinforcement learning on MuJoCo simulated robotic control tasks.

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