Consensus-based Recursive Multi-Output Gaussian Process
Abstract
Multi-output Gaussian Processes provide principled uncertainty-aware learning of vector-valued fields but are difficult to deploy in large-scale, distributed, and streaming settings due to their computational and centralized nature. This paper proposes a Consensus-based Recursive Multi-Output Gaussian Process (CRMGP) framework that combines recursive inference on shared basis vectors with neighbour-to-neighbour information-consensus updates. The resulting method supports parallel, fully distributed learning with bounded per-step computation while preserving inter-output correlations and calibrated uncertainty. Experiments on synthetic wind fields and real LiDAR data demonstrate that CRMGP achieves competitive predictive performance and reliable uncertainty calibration, offering a scalable alternative to centralized Gaussian process models for multi-agent sensing applications.
Cite
@article{arxiv.2604.10146,
title = {Consensus-based Recursive Multi-Output Gaussian Process},
author = {Yogesh Prasanna Kumar Rao and Tamas Keviczky and Raj Thilak Rajan},
journal= {arXiv preprint arXiv:2604.10146},
year = {2026}
}
Comments
Submitted to International Workshop on Signal Processing and Artificial Intelligence in Wireless Communications (IEEE SPAWC 2026)