Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation
Not on the map. Why?
Abstract (arXiv)
This article investigates the origin of numerical issues in maximum likelihood parameter estimation for Gaussian process (GP) interpolation and investigates simple but effective strategies for improving commonly used open-source software implementations. This work targets a basic problem but a host of studies, particularly in the literature of Bayesian optimization, rely on off-the-shelf GP implementations. For the conclusions of these studies to be reliable and reproducible, robust GP implementations are critical.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 4 of its 43 references that are in the map. First posted on arXiv 24 Jan 2021.
Builds on
4 of 43 references.
References · 4
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
- 2016GPflow: A Gaussian process library using TensorFlowcited by 139
- 2020Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradientscited by 5
Built on by
Citing papers per year: 2022: 3; 2023: 3; 2024: 1.
7 papers.
- 2022Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Treescited by 5
- 2022Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learningcited by 4
- 2022Nonparametric Multi-shape Modeling with Uncertainty Quantificationcited by 2
- 2023A Global-Local Approximation Framework for Large-Scale Gaussian Process Modelingcited by 2
- 2023Rational kernel-based interpolation for complex-valued frequency response functionscited by 1
- 2024A class of modular and flexible covariate-based covariance functions for nonstationary spatial modelingcited by 1
- 2023Index-aware learning of circuitscited by 0
- 2024A class of modular and flexible covariate-based covariance functions for nonstationary spatial modeling22 Oct 2024
- 2023Index-aware learning of circuits2 Sep 2023
- 2023Rational kernel-based interpolation for complex-valued frequency response functions25 Jul 2023
- 2023A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling17 May 2023
- 2022Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees14 Oct 2022
- 2022Nonparametric Multi-shape Modeling with Uncertainty Quantification18 Jun 2022
- 2022Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning6 Mar 2022