Locally Smoothed Gaussian Process Regression
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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.
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.
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12 of 39 references.
References · 12
- 2015Deep Kernel Learningcited by 445
- 2013Gaussian Processes for Big Datacited by 383
- 2018Gaussian Processes and Kernel Methods: A Review on Connections and Equivalencescited by 192
- 2013Local Gaussian process approximation for large computer experimentscited by 59
- 2016Preconditioning Kernel Matricescited by 33
- 2016Random Feature Expansions for Deep Gaussian Processescited by 32
- 2015Optimal Learning Rates for Localized SVMscited by 13
- 2016AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Modelscited by 6
- 2021ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitionscited by 6
- 2015Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)cited by 3
- 2016Kernel Ridge Regression via Partitioningcited by 2
- 2014Local Gaussian Regressioncited by 1
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