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

arXiv: https://arxiv.org/abs/1806.00720 · HTML: https://mlsanity.com/paper/1806.00720/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2018.

## Built on by (most cited first, 30 of 33)

- [Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space](https://mlsanity.com/paper/2312.09817/) (2023)
- [Hierarchical Policy Blending as Inference for Reactive Robot Control](https://mlsanity.com/paper/2210.07890/) (2022)
- [Correlated Product of Experts for Sparse Gaussian Process Regression](https://mlsanity.com/paper/2112.09519/) (2021)
- [Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains](https://mlsanity.com/paper/2202.12636/) (2022)
- [Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey](https://mlsanity.com/paper/2103.10134/) (2021)
- [Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression](https://mlsanity.com/paper/2306.14731/) (2023)
- [Lightweight Distributed Gaussian Process Regression for Online Machine Learning](https://mlsanity.com/paper/2105.04738/) (2021)
- [Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning](https://mlsanity.com/paper/2203.02865/) (2022)
- [Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version](https://mlsanity.com/paper/2508.03679/) (2025)
- [Mixtures of Gaussian Process Experts with SMC2](https://mlsanity.com/paper/2208.12830/) (2022)
- [Bayesian data fusion with shared priors](https://mlsanity.com/paper/2212.07311/) (2022)
- [Whom to Trust? Elective Learning for Distributed Gaussian Process Regression](https://mlsanity.com/paper/2402.03014/) (2024)
- [Healing Products of Gaussian Processes](https://mlsanity.com/paper/2102.07106/) (2021)
- [Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions](https://mlsanity.com/paper/2503.09418/) (2025)
- [Robust, Online, and Adaptive Decentralized Gaussian Processes](https://mlsanity.com/paper/2509.18011/) (2025)
- [Gaussian Experts Selection using Graphical Models](https://mlsanity.com/paper/2102.01496/) (2021)
- [Temporal Gaussian Process Regression in Logarithmic Time](https://mlsanity.com/paper/2102.09964/) (2021)
- [Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems](https://mlsanity.com/paper/2106.05848/) (2021)
- [Learning with Subset Stacking](https://mlsanity.com/paper/2112.06251/) (2021)
- [Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks](https://mlsanity.com/paper/2409.07652/) (2024)
- [GPTreeO: An R package for continual regression with dividing local Gaussian processes](https://mlsanity.com/paper/2410.01024/) (2024)
- [Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems](https://mlsanity.com/paper/2502.05301/) (2025)
- [When Are Two Scores Better Than One? Investigating Ensembles of Diffusion Models](https://mlsanity.com/paper/2601.11444/) (2026)
- [Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Version](https://mlsanity.com/paper/2601.14275/) (2026)
- [Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes](https://mlsanity.com/paper/2202.03287/) (2022)
- [Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification](https://mlsanity.com/paper/2211.09940/) (2022)
- [A Bayesian Committee Machine Potential for Oxygen-containing Organic Compounds](https://mlsanity.com/paper/2403.01158/) (2024)
- [Aggregation Models with Optimal Weights for Distributed Gaussian Processes](https://mlsanity.com/paper/2408.00955/) (2024)
- [Byzantine-resilient federated online learning for Gaussian process regression](https://mlsanity.com/paper/2507.14021/) (2025)
- [Offline Multi-Task Multi-Objective Data-Driven Evolutionary Algorithm with Language Surrogate Model and Implicit Q-Learning](https://mlsanity.com/paper/2512.15149/) (2025)

## 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.