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Poster
Conditioning Sparse Variational Gaussian Processes for Online Decision-making
Wesley Maddox · Samuel Stanton · Andrew Wilson

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ Virtual
in Poster Session 1 »
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.
Keywords: Optimization · Reinforcement Learning and Planning · Kernel Methods · Active Learning

Author Information

Wesley Maddox (New York University)
Samuel Stanton (New York University)

ML Scientist at Genentech Early Research and Development (gRED). Building ML systems for scientific discovery in biotech.

Andrew Wilson (New York University)
Andrew Wilson

I am a professor of machine learning at New York University.

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