mlsanityData to 14 Oct 2026

Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation

arXiv 2205.10879 ↗First posted 22 May 2022cs.LG cs.NA math.NAcited by 1

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Abstract (arXiv)

Gaussian processes (GPs) are Bayesian non-parametric models useful in a myriad of applications. Despite their popularity, the cost of GP predictions (quadratic storage and cubic complexity with respect to the number of training points) remains a hurdle in applying GPs to large data. We present a fast posterior mean prediction algorithm called FastMuyGPs to address this shortcoming. FastMuyGPs is based upon the MuyGPs hyperparameter estimation algorithm and utilizes a combination of leave-one-out cross-validation, batching, nearest neighbors sparsification, and precomputation to provide scalable, fast GP prediction. We demonstrate several benchmarks wherein FastMuyGPs prediction attains superior accuracy and competitive or superior runtime to both deep neural networks and state-of-the-art scalable GP algorithms.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 61 references that are in the map. First posted on arXiv 22 May 2022.

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21 of 61 references.

References · 21

  1. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 861
  2. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  3. 2015Deep Kernel Learningcited by 445
  4. 2019Reinforcement Learning in Healthcare: A Surveycited by 310
  5. 2014Scalable Variational Gaussian Process Classificationcited by 228
  6. 2018When Gaussian Process Meets Big Data: A Review of Scalable GPscited by 206
  7. 2015Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)cited by 177
  8. 2016GPflow: A Gaussian process library using TensorFlowcited by 140
  9. 2019Exact Gaussian Processes on a Million Data Pointscited by 119
  10. 2014Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasetscited by 72
  11. 2017A Case Study Competition Among Methods for Analyzing Large Spatial Datacited by 44
  12. 2015The Variational Gaussian Processcited by 30
9 more
  1. 2016Learning Scalable Deep Kernels with Recurrent Structurecited by 22
  2. 2014Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Modelscited by 19
  3. 2020Locally induced Gaussian processes for large-scale simulation experimentscited by 11
  4. 2017Asynchronous Distributed Variational Gaussian Processes for Regressioncited by 8
  5. 2021MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validationcited by 8
  6. 2019On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysiscited by 4
  7. 2014Gaussian Process Models with Parallelization and GPU accelerationcited by 3
  8. 2017Spectral Density Estimation for Random Fields via Periodic Embeddingscited by 2
  9. 2017Forecasting of commercial sales with large scale Gaussian Processescited by 1

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Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)cited by 1