mlsanityData to 7 Oct 2026

Streaming Sparse Gaussian Process Approximations

arXiv 1705.07131 ↗First posted 19 May 2017stat.MLcited by 41

Not on the map. Why?

Abstract (arXiv)

Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which both the posterior distribution over function values and the hyperparameter estimates are updated in an online fashion. The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive. This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing methods for learning hyperparameters and optimising pseudo-input locations. The proposed framework is assessed using synthetic and real-world datasets.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 May 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 8; 2023: 7; 2024: 10; 2025: 5; 2026: 2.

41 papers.

Sort:

By area:

  1. 2022Information-theoretic Online Memory Selection for Continual Learningcited by 42
  2. 2023Continual Learning via Sequential Function-Space Variational Inferencecited by 28
  3. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  4. 2021Kernel Interpolation for Scalable Online Gaussian Processescited by 18
  5. 2021Incremental Ensemble Gaussian Processescited by 15
  6. 2022Partitioned Variational Inference: A Framework for Probabilistic Federated Learningcited by 13
  7. 2023Memory-Based Dual Gaussian Processes for Sequential Learningcited by 11
  8. 2021Adaptive Inducing Points Selection For Gaussian Processescited by 8
  9. 2021Correlated Product of Experts for Sparse Gaussian Process Regressioncited by 7
  10. 2022Fantasizing with Dual GPs in Bayesian Optimization and Active Learningcited by 7
  11. 2025Knowledge Adaptation as Posterior Correctioncited by 6
  12. 2024Dynamic Online Ensembles of Basis Expansionscited by 5
29 more
  1. 2021Hida-Mat'ern Kernelcited by 4
  2. 2022Online Time Series Anomaly Detection with State Space Gaussian Processescited by 4
  3. 2022Data-driven Science and Machine Learning Methods in Laser-Plasma Physicscited by 4
  4. 2024Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equationscited by 4
  5. 2024Streaming Bayes GFlowNetscited by 4
  6. 2023Adaptive Sparse Gaussian Processcited by 3
  7. 2022Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processescited by 2
  8. 2023Active Cost-aware Labeling of Streaming Datacited by 2
  9. 2025Recurrent Memory for Online Interdomain Gaussian Processescited by 2
  10. 2025Robust, Online, and Adaptive Decentralized Gaussian Processescited by 2
  11. 2026Incremental Transformer Neural Processescited by 2
  12. 2021Active and sparse methods in smoothed model checkingcited by 1
  13. 2021Modular Gaussian Processes for Transfer Learningcited by 1
  14. 2022Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processescited by 1
  15. 2023Beyond Intuition, a Framework for Applying GPs to Real-World Datacited by 1
  16. 2024Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Modelscited by 1
  17. 2024Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?cited by 1
  18. 2024Likelihood approximations via Gaussian approximate inferencecited by 1
  19. 2024Prediction of Acoustic Communication Performance for AUVs using Gaussian Process Classificationcited by 1
  20. 2021Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inferencecited by 0
  21. 2022Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision makingcited by 0
  22. 2023Greedy online change point detectioncited by 0
  23. 2023Online Student-t Processes with an Overall-local Scale Structure for Modelling Non-stationary Datacited by 0
  24. 2024Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observationscited by 0
  25. 2024Incremental Structure Discovery of Classification via Sequential Monte Carlocited by 0
  26. 2024Recursive Gaussian Process State Space Modelcited by 0
  27. 2025Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learningcited by 0
  28. 2025DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Processcited by 0
  29. 2026Fast rates in Bayesian online learning with approximate posteriorscited by 0