# Locally induced Gaussian processes for large-scale simulation experiments

arXiv: https://arxiv.org/abs/2008.12857 · HTML: https://mlsanity.com/paper/2008.12857/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation](https://mlsanity.com/paper/2301.10123/) (2023)
- [Multitask methods for predicting molecular properties from heterogeneous data](https://mlsanity.com/paper/2401.17898/) (2024)
- [Sensitivity Prewarping for Local Surrogate Modeling](https://mlsanity.com/paper/2101.06296/) (2021)
- [A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling](https://mlsanity.com/paper/2305.10158/) (2023)
- [Modular Jump Gaussian Processes](https://mlsanity.com/paper/2505.15557/) (2025)
- [Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation](https://mlsanity.com/paper/2205.10879/) (2022)
- [Correspondence of NNGP Kernel and the Matern Kernel](https://mlsanity.com/paper/2410.08311/) (2024)
- [Mind the Jumps: A Scalable Robust Local Gaussian Process for Multidimensional Response Surfaces with Discontinuities](https://mlsanity.com/paper/2512.12574/) (2025)
- [Beyond State Space Representation: A General Theory for Kernel Packets](https://mlsanity.com/paper/2402.04022/) (2024)
- [Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner](https://mlsanity.com/paper/2408.00206/) (2024)
- [Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates](https://mlsanity.com/paper/2602.21408/) (2026)

## Abstract (arXiv)

Gaussian processes (GPs) serve as flexible surrogates for complex surfaces, but buckle under the cubic cost of matrix decompositions with big training data sizes. Geospatial and machine learning communities suggest pseudo-inputs, or inducing points, as one strategy to obtain an approximation easing that computational burden. However, we show how placement of inducing points and their multitude can be thwarted by pathologies, especially in large-scale dynamic response surface modeling tasks. As remedy, we suggest porting the inducing point idea, which is usually applied globally, over to a more local context where selection is both easier and faster. In this way, our proposed methodology hybridizes global inducing point and data subset-based local GP approximation. A cascade of strategies for planning the selection of local inducing points is provided, and comparisons are drawn to related methodology with emphasis on computer surrogate modeling applications. We show that local inducing points extend their global and data-subset component parts on the accuracy--computational efficiency frontier. Illustrative examples are provided on benchmark data and a large-scale real-simulation satellite drag interpolation problem.