Provably Reliable Large-Scale Sampling from Gaussian Processes

• URL: http://arxiv.org/abs/2211.08036v1
• Date: Tue, 15 Nov 2022 10:36:21 GMT
• Title: Provably Reliable Large-Scale Sampling from Gaussian Processes
• Authors: Anthony Stephenson, Robert Allison, Edward Pyzer-Knapp
• Abstract summary: We demonstrate how to generate a size (n) sample from approximate Gaussian process (GP) models. We provide guarantees that, with high probability, the sample is indistinguishable from a sample from the desired GP.
• Score: 3.6417668958891793
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When comparing approximate Gaussian process (GP) models, it can be helpful to be able to generate data from any GP. If we are interested in how approximate methods perform at scale, we may wish to generate very large synthetic datasets to evaluate them. Na\"{i}vely doing so would cost \(\mathcal{O}(n^3)\) flops and \(\mathcal{O}(n^2)\) memory to generate a size \(n\) sample. We demonstrate how to scale such data generation to large \(n\) whilst still providing guarantees that, with high probability, the sample is indistinguishable from a sample from the desired GP.

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