mlsanityData to 7 Oct 2026

Kernel Interpolation for Scalable Online Gaussian Processes

arXiv 2103.01454 ↗First posted 2 Mar 2021stat.ML cs.LGcited by 18

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Abstract (arXiv)

Gaussian processes (GPs) provide a gold standard for performance in online settings, such as sample-efficient control and black box optimization, where we need to update a posterior distribution as we acquire data in a sequential fashion. However, updating a GP posterior to accommodate even a single new observation after having observed n points incurs at least O(n) computations in the exact setting. We show how to use structured kernel interpolation to efficiently recycle computations for constant-time O(1) online updates with respect to the number of points n, while retaining exact inference. We demonstrate the promise of our approach in a range of online regression and classification settings, Bayesian optimization, and active sampling to reduce error in malaria incidence forecasting. Code is available at https://github.com/wjmaddox/onlinegp.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

17 of 42 references.

References · 17

  1. 2018A Tutorial on Bayesian Optimizationcited by 734
  2. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
  3. 2015Deep Kernel Learningcited by 440
  4. 2013Gaussian Processes for Big Datacited by 377
  5. 2015Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)cited by 175
  6. 2013Gaussian Process Kernels for Pattern Discovery and Extrapolationcited by 131
  7. 2019Exact Gaussian Processes on a Million Data Pointscited by 118
  8. 2017Constrained Bayesian Optimization with Noisy Experimentscited by 103
  9. 2016Understanding Probabilistic Sparse Gaussian Process Approximationscited by 68
  10. 2018Dirichlet-based Gaussian Processes for Large-scale Calibrated Classificationcited by 50
  11. 2018Constant-Time Predictive Distributions for Gaussian Processescited by 48
  12. 2017Streaming Sparse Gaussian Process Approximationscited by 41
5 more
  1. 2019Randomly Projected Additive Gaussian Processes for Regressioncited by 19
  2. 2020Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneckcited by 10
  3. 2017PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising Systemcited by 9
  4. 2018Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)cited by 9
  5. 2014GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Modelcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 5; 2024: 4; 2025: 2; 2026: 2.

18 papers.

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  1. 2022Bayesian Optimization with Conformal Prediction Setscited by 27
  2. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  3. 2024Operator Learning with Gaussian Processescited by 18
  4. 2021Data Fusion with Latent Map Gaussian Processescited by 16
  5. 2021Incremental Ensemble Gaussian Processescited by 15
  6. 2023Kernel Regression with Infinite-Width Neural Networks on Millions of Examplescited by 9
  7. 2024Dynamic Online Ensembles of Basis Expansionscited by 5
  8. 2023Adaptive Sparse Gaussian Processcited by 3
  9. 2025Recurrent Memory for Online Interdomain Gaussian Processescited by 2
  10. 2026Incremental Transformer Neural Processescited by 2
  11. 2022Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision makingcited by 0
  12. 2023Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regressioncited by 0
6 more
  1. 2023Online Student-t Processes with an Overall-local Scale Structure for Modelling Non-stationary Datacited by 0
  2. 2023A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulationcited by 0
  3. 2024Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditionercited by 0
  4. 2024Tensor network square root Kalman filter for online Gaussian process regressioncited by 0
  5. 2025DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Processcited by 0
  6. 2026Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimizationcited by 0