Congratulations to Compass PhD student, Anthony Stephenson, who along with his supervisors, Robert Allison and Ed Pyzer-Knapp (IBM Research) has had their paper Provably Reliable Large-Scale Sampling from Gaussian Processes accepted to be published at NeurIPS 2022.
Anthony mentions:
“Gaussian processes are a highly flexible class of non-parametric Bayesian models used in a variety of applications. In their exact form they provide principled uncertainty representations, at the expense of poor scalability (O(n^3)) with the number of training points. As a result, many approximate methods have been proposed to try and address this. We raise the question of how to assess the performance of such methods. The most obvious approach is to generate data from the exact GP model and then benchmark performance metrics of the approximations against the data generating process. Unfortunately, generating data from an exact GP is also in general an O(n^3) problem. We address this limitation by demonstrating how tunable parameters controlling the fidelity of inexact methods of drawing samples can be chosen to ensure that their samples are, with high probability, indistinguishable from genuine data from the exact GP.”
For more information: [2211.08036] Provably Reliable Large-Scale Sampling from Gaussian Processes (arxiv.org)