# Consensus-based Recursive Multi-Output Gaussian Process

arXiv: https://arxiv.org/abs/2604.10146 · HTML: https://mlsanity.com/paper/2604.10146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 11 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [Distributed Gaussian Processes](https://mlsanity.com/paper/1502.02843/) (2015)
- [Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment](https://mlsanity.com/paper/2007.14052/) (2020)

## Built on by (most cited first, 1 of 1)

- [Resource-Efficient Distributed Recursive Gaussian Processes](https://mlsanity.com/paper/2609.26979/) (2026)

## Abstract (arXiv)

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.