# Locally Smoothed Gaussian Process Regression

arXiv: https://arxiv.org/abs/2210.09998 · HTML: https://mlsanity.com/paper/2210.09998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences](https://mlsanity.com/paper/1807.02582/) (2018)
- [Local Gaussian process approximation for large computer experiments](https://mlsanity.com/paper/1303.0383/) (2013)
- [Preconditioning Kernel Matrices](https://mlsanity.com/paper/1602.06693/) (2016)
- [Random Feature Expansions for Deep Gaussian Processes](https://mlsanity.com/paper/1610.04386/) (2016)
- [Optimal Learning Rates for Localized SVMs](https://mlsanity.com/paper/1507.06615/) (2015)
- [AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models](https://mlsanity.com/paper/1610.05392/) (2016)
- [ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions](https://mlsanity.com/paper/2106.12231/) (2021)
- [Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)](https://mlsanity.com/paper/1501.05427/) (2015)
- [Kernel Ridge Regression via Partitioning](https://mlsanity.com/paper/1608.01976/) (2016)
- [Local Gaussian Regression](https://mlsanity.com/paper/1402.0645/) (2014)

## Abstract (arXiv)

We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. Through a set of experiments, we demonstrate the competitive performance of the proposed approach compared to full GPR, other localized models, and deep Gaussian processes. Crucially, these performances are obtained with considerable speedups compared to standard global GPR due to the sparsification effect of the Gram matrix induced by the localization operation.