# A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling

arXiv: https://arxiv.org/abs/2305.10158 · HTML: https://mlsanity.com/paper/2305.10158/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 55 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 11 of 11)

- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Scalable Variational Gaussian Process Classification](https://mlsanity.com/paper/1411.2005/) (2014)
- [Distributed Gaussian Processes](https://mlsanity.com/paper/1502.02843/) (2015)
- [Local Gaussian process approximation for large computer experiments](https://mlsanity.com/paper/1303.0383/) (2013)
- [Support points](https://mlsanity.com/paper/1609.01811/) (2016)
- [SPlit: An Optimal Method for Data Splitting](https://mlsanity.com/paper/2012.10945/) (2020)
- [Locally induced Gaussian processes for large-scale simulation experiments](https://mlsanity.com/paper/2008.12857/) (2020)
- [Data Twinning](https://mlsanity.com/paper/2110.02927/) (2021)
- [Patchwork Kriging for Large-scale Gaussian Process Regression](https://mlsanity.com/paper/1701.06655/) (2017)
- [Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation](https://mlsanity.com/paper/2101.09747/) (2021)
- [Uncertainty Quantification of a Computer Model for Binary Black Hole Formation](https://mlsanity.com/paper/2106.01552/) (2021)

## Built on by (most cited first, 2 of 2)

- [Fast Gaussian Process Approximations for Autocorrelated Data](https://mlsanity.com/paper/2512.02925/) (2025)
- [Spatio-Temporal Gaussian Process for Building Terrain-Incorporating Wind Power Curves](https://mlsanity.com/paper/2607.00051/) (2026)

## Abstract (arXiv)

In this work, we propose a novel framework for large-scale Gaussian process (GP) modeling. Contrary to the global, and local approximations proposed in the literature to address the computational bottleneck with exact GP modeling, we employ a combined global-local approach in building the approximation. Our framework uses a subset-of-data approach where the subset is a union of a set of global points designed to capture the global trend in the data, and a set of local points specific to a given testing location to capture the local trend around the testing location. The correlation function is also modeled as a combination of a global, and a local kernel. The performance of our framework, which we refer to as TwinGP, is on par or better than the state-of-the-art GP modeling methods at a fraction of their computational cost.