mlsanityData to 7 Oct 2026

Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression

arXiv 1806.00720 ↗First posted 3 Jun 2018stat.ML cs.LGcited by 33

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Abstract (arXiv)

In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts. The state-of-the-art aggregation models, however, either provide inconsistent predictions or require time-consuming aggregation process. We first prove the inconsistency of typical aggregations using disjoint or random data partition, and then present a consistent yet efficient aggregation model for large-scale GP. The proposed model inherits the advantages of aggregations, e.g., closed-form inference and aggregation, parallelization and distributed computing. Furthermore, theoretical and empirical analyses reveal that the new aggregation model performs better due to the consistent predictions that converge to the true underlying function when the training size approaches infinity.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2018.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 2; 2024: 5; 2025: 6; 2026: 5.

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  1. 2023Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Spacecited by 10
  2. 2022Hierarchical Policy Blending as Inference for Reactive Robot Controlcited by 8
  3. 2021Correlated Product of Experts for Sparse Gaussian Process Regressioncited by 7
  4. 2022Learning Multi-Task Gaussian Process Over Heterogeneous Input Domainscited by 7
  5. 2021Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Surveycited by 5
  6. 2023Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regressioncited by 5
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  9. 2025Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Versioncited by 4
  10. 2022Mixtures of Gaussian Process Experts with SMC2cited by 3
  11. 2022Bayesian data fusion with shared priorscited by 3
  12. 2024Whom to Trust? Elective Learning for Distributed Gaussian Process Regressioncited by 3
21 more
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  4. 2021Gaussian Experts Selection using Graphical Modelscited by 1
  5. 2021Temporal Gaussian Process Regression in Logarithmic Timecited by 1
  6. 2021Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systemscited by 1
  7. 2021Learning with Subset Stackingcited by 1
  8. 2024Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networkscited by 1
  9. 2024GPTreeO: An R package for continual regression with dividing local Gaussian processescited by 1
  10. 2025Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systemscited by 1
  11. 2026When Are Two Scores Better Than One? Investigating Ensembles of Diffusion Modelscited by 1
  12. 2026Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Versioncited by 1
  13. 2022Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processescited by 0
  14. 2022Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classificationcited by 0
  15. 2024A Bayesian Committee Machine Potential for Oxygen-containing Organic Compoundscited by 0
  16. 2024Aggregation Models with Optimal Weights for Distributed Gaussian Processescited by 0
  17. 2025Byzantine-resilient federated online learning for Gaussian process regressioncited by 0
  18. 2025Offline Multi-Task Multi-Objective Data-Driven Evolutionary Algorithm with Language Surrogate Model and Implicit Q-Learningcited by 0
  19. 2026Distributed Quantum Gaussian Processes for Multi-Agent Systemscited by 0
  20. 2026Beyond Mixtures and Products for Ensemble Aggregation: A Likelihood Perspective on Generalized Meanscited by 0
  21. 2026Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systemscited by 0