# Provably Reliable Large-Scale Sampling from Gaussian Processes

arXiv: https://arxiv.org/abs/2211.08036 · HTML: https://mlsanity.com/paper/2211.08036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions](https://mlsanity.com/paper/1502.06800/) (2015)
- [Efficiently Sampling Functions from Gaussian Process Posteriors](https://mlsanity.com/paper/2002.09309/) (2020)
- [Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond](https://mlsanity.com/paper/2004.11154/) (2020)
- [On the Error of Random Fourier Features](https://mlsanity.com/paper/1506.02785/) (2015)
- [Optimal Rates for Random Fourier Features](https://mlsanity.com/paper/1506.02155/) (2015)
- [Approximation beats concentration? An approximation view on inference with smooth radial kernels](https://mlsanity.com/paper/1801.03437/) (2018)
- [Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization](https://mlsanity.com/paper/2006.11267/) (2020)
- [Fast and Accurate Gaussian Kernel Ridge Regression Using Matrix Decompositions for Preconditioning](https://mlsanity.com/paper/1905.10587/) (2019)

## Built on by (most cited first, 1 of 1)

- [Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression](https://mlsanity.com/paper/2306.14731/) (2023)

## Abstract (arXiv)

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"ively doing so would cost (O(n3)) flops and (O(n2)) 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.