@inproceedings{63515a09589d43e68f853ec2a2799fef,
title = "Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems",
abstract = "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.",
keywords = "Gaussian processes, distributed learning, multi-agent systems",
author = "Fernando Llorente and Daniel Waxman and Djuri{\'c}, \{Petar M.\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 ; Conference date: 06-04-2025 Through 11-04-2025",
year = "2025",
doi = "10.1109/ICASSP49660.2025.10889417",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Rao, \{Bhaskar D\} and Isabel Trancoso and Gaurav Sharma and Mehta, \{Neelesh B.\}",
booktitle = "2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings",
}