Data to 7 Oct 2026

Distributed Gaussian Processes

arXiv 1502.02843 ↗First posted 10 Feb 2015stat.MLcited by 84

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Abstract (arXiv)

To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or variational parameters. The key idea is to recursively distribute computations to independent computational units and, subsequently, recombine them to form an overall result. Efficient closed-form inference allows for straightforward parallelisation and distributed computations with a small memory footprint. The rBCM is independent of the computational graph and can be used on heterogeneous computing infrastructures, ranging from laptops to clusters. With sufficient computing resources our distributed GP model can handle arbitrarily large data sets.

Key facts. Cited by 84 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2015.

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Citing papers per year: 2017: 1; 2018: 0; 2019: 1; 2020: 1; 2021: 16; 2022: 16; 2023: 15; 2024: 9; 2025: 14; 2026: 11.

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2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
2023Model Merging by Uncertainty-Based Gradient Matchingcited by 77
2022Residual Mixture of Expertscited by 39
2025MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidancecited by 32
2021Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networkscited by 17
2025MoE-Loco: Mixture of Experts for Multitask Locomotioncited by 17
2021Bias-Free Scalable Gaussian Processes via Randomized Truncationscited by 15
2021Incremental Ensemble Gaussian Processescited by 15
2025Mixture of Lookup Expertscited by 11
2023Stochastic Marginal Likelihood Gradients using Neural Tangent Kernelscited by 10
2024Training-efficient density quantum machine learningcited by 10
2021The Fast Kernel Transformcited by 8

38 more

2021Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regressioncited by 8
2021Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modelingcited by 8
2022Scalable Gaussian-process regression and variable selection using Vecchia approximationscited by 8
2022Hierarchical Policy Blending as Inference for Reactive Robot Controlcited by 8
2021Correlated Product of Experts for Sparse Gaussian Process Regressioncited by 7
2025MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Modelscited by 7
2023Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processescited by 6
2019Hierarchical Routing Mixture of Expertscited by 5
2020Gaussian-Process-based Robot Learning from Demonstrationcited by 5
2021Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Surveycited by 5
2022Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Processcited by 5
2023Sequential Estimation of Gaussian Process-based Deep State-Space Modelscited by 5
2023Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regressioncited by 5
2024No Need to Talk: Asynchronous Mixture of Language Modelscited by 5
2021Distributed Nonparametric Function Estimation: Optimal Rate of Convergence and Cost of Adaptationcited by 4
2022Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learningcited by 4
2023Actually Sparse Variational Gaussian Processescited by 4
2024Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologiescited by 4
2025Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Aheadcited by 4
2025Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Versioncited by 4
2021GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Datacited by 3
2021Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefitscited by 3
2022B'ezier Curve Gaussian Processescited by 3
2022A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Methodcited by 3
2022Mixtures of Gaussian Process Experts with SMC2cited by 3
2022Bayesian data fusion with shared priorscited by 3
2023Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networkscited by 3
2023Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategycited by 3
2024Whom to Trust? Elective Learning for Distributed Gaussian Process Regressioncited by 3
2025All Emulators are Wrong, Many are Useful, and Some are More Useful Than Others: A Reproducible Comparison of Computer Model Surrogatescited by 3
2021Healing Products of Gaussian Processescited by 2
2022Adaptive Cholesky Gaussian Processescited by 2
2022Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processescited by 2
2023Multi-view Kernel PCA for Time series Forecastingcited by 2
2023A Global-Local Approximation Framework for Large-Scale Gaussian Process Modelingcited by 2
2024Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regressioncited by 2
2025Robust, Online, and Adaptive Decentralized Gaussian Processescited by 2
2025Fast Gaussian Process Approximations for Autocorrelated Datacited by 2

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