Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems
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Abstract (arXiv)
Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles of) kernel machines in the distributed setting, Bayesian solutions are much more limited. We introduce a fully decentralized, asymptotically exact solution to computing the random feature approximation of Gaussian processes. We further address the choice of hyperparameters by introducing an ensembling scheme for Bayesian multiple kernel learning based on online Bayesian model averaging. The resulting algorithm is tested against Bayesian and frequentist methods on simulated and real-world datasets.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 7 Feb 2025.
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12 of 28 references.
References · 12
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- 2016Orthogonal Random Featurescited by 91
- 2014Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernelscited by 42
- 2018Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regressioncited by 33
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- 2022Fusion of Probability Density Functionscited by 22
- 2016Nested Kriging predictions for datasets with large number of observationscited by 18
- 2021Incremental Ensemble Gaussian Processescited by 15
- 2017Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Featurescited by 11
- 2021Distributed Online Learning with Multiple Kernelscited by 9
- 2024Dynamic Online Ensembles of Basis Expansionscited by 5
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Citing papers per year: 2025: 1.
1 paper.