mlsanityData to 7 Oct 2026

Local Gaussian process approximation for large computer experiments

arXiv 1303.0383 ↗First posted 2 Mar 2013stat.ME stat.COcited by 59

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Abstract (arXiv)

We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of local sequential design schemes that dynamically define the support of a Gaussian process predictor based on a local subset of the data. We further derive expressions for fast sequential updating of all needed quantities as the local designs are built-up iteratively. Then we show how independent application of our local design strategy across the elements of a vast predictive grid facilitates a trivially parallel implementation. The end result is a global predictor able to take advantage of modern multicore architectures, while at the same time allowing for a nonstationary modeling feature as a bonus. We demonstrate our method on two examples utilizing designs sized in the thousands, and tens of thousands of data points. Comparisons are made to the method of compactly supported covariances.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2013.

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Citing papers per year: 2021: 15; 2022: 8; 2023: 8; 2024: 12; 2025: 13; 2026: 3.

59 papers.

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  1. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
  2. 2021Latent Map Gaussian Processes for Mixed Variable Metamodelingcited by 23
  3. 2021Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networkscited by 17
  4. 2021Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factorscited by 11
  5. 2021Entropy-based adaptive design for contour finding and estimating reliabilitycited by 10
  6. 2022Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Mat'ern Correlationscited by 10
  7. 2022Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priorscited by 9
  8. 2021MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validationcited by 8
  9. 2021A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABCcited by 8
  10. 2021Non-smooth Bayesian Optimization in Tuning Problemscited by 8
  11. 2022Variational Nearest Neighbor Gaussian Processcited by 8
  12. 2023A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processescited by 8
38 more
  1. 2024Scalable Bayesian Optimization via Focalized Sparse Gaussian Processescited by 8
  2. 2021Scalable Cross Validation Losses for Gaussian Process Modelscited by 6
  3. 2023Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian Processescited by 6
  4. 2021Input Dependent Sparse Gaussian Processescited by 5
  5. 2023Active Learning of Piecewise Gaussian Process Surrogatescited by 5
  6. 2024Voronoi Candidates for Bayesian Optimizationcited by 5
  7. 2021Sensitivity Prewarping for Local Surrogate Modelingcited by 4
  8. 2021Learning particle swarming models from data with Gaussian processescited by 3
  9. 2025All Emulators are Wrong, Many are Useful, and Some are More Useful Than Others: A Reproducible Comparison of Computer Model Surrogatescited by 3
  10. 2021High dimensional Bayesian Optimization Algorithm for Complex System in Time Seriescited by 2
  11. 2022Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functionscited by 2
  12. 2023Trajectory-oriented optimization of stochastic epidemiological modelscited by 2
  13. 2023A Global-Local Approximation Framework for Large-Scale Gaussian Process Modelingcited by 2
  14. 2024Targeted Variance Reduction: Robust Bayesian Optimization of Black-Box Simulators with Noise Parameterscited by 2
  15. 2024Posterior Covariance Structures in Gaussian Processescited by 2
  16. 2025Modular Jump Gaussian Processescited by 2
  17. 2025Fast Gaussian Process Approximations for Autocorrelated Datacited by 2
  18. 2021Gaussian Process Subspace Regression for Model Reductioncited by 1
  19. 2022A model aggregation approach for high-dimensional large-scale optimizationcited by 1
  20. 2022Dynamic Bayesian Learning for Spatiotemporal Mechanistic Modelscited by 1
  21. 2024Correspondence of NNGP Kernel and the Matern Kernelcited by 1
  22. 2025Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functionscited by 1
  23. 2025Bayesian Adaptive Polynomial Chaos Expansionscited by 1
  24. 2025Mind the Jumps: A Scalable Robust Local Gaussian Process for Multidimensional Response Surfaces with Discontinuitiescited by 1
  25. 2021Scalable Statistical Inference of Photometric Redshift via Data Subsamplingcited by 0
  26. 2021Gaussian Process Model for Estimating Piecewise Continuous Regression Functionscited by 0
  27. 2022Optimal Sensor Placement in Body Surface Networks using Gaussian Processescited by 0
  28. 2022Locally Smoothed Gaussian Process Regressioncited by 0
  29. 2023Privacy-aware Gaussian Process Regressioncited by 0
  30. 2023Surrogate Active Subspaces for Jump-Discontinuous Functionscited by 0
  31. 2024Beyond State Space Representation: A General Theory for Kernel Packetscited by 0
  32. 2024Bayesian Optimization that Limits Search Region to Lower Dimensions Utilizing Local GPRcited by 0
  33. 2024Further Understanding of a Local Gaussian Process Approximation: Characterising Convergence in the Finite Regimecited by 0
  34. 2024Gaussian Process Model with Tensorial Inputs and Its Application to the Design of 3D Printed Antennascited by 0
  35. 2024Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditionercited by 0
  36. 2024Linear cost and exponentially convergent approximation of Gaussian Mat'ern processes on intervalscited by 0
  37. 2024When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquercited by 0
  38. 2025Optimal Kernel Learning for Gaussian Process Models with High-Dimensional Inputcited by 0

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