Consensus-based Recursive Multi-Output Gaussian Process

2026-04-11

AI Summary

This work addresses the challenges of applying multi-output Gaussian processes to large-scale distributed and streaming settings, where high computational complexity and centralized architectures hinder scalability. The authors propose the first fully decentralized learning framework that integrates recursive inference with a neighbor consensus mechanism. By leveraging shared inducing vectors and distributed Bayesian inference, the approach maintains inter-output correlations and well-calibrated uncertainty estimates while ensuring bounded per-step computation and enabling parallel processing. Experimental results on both synthetic wind field data and real-world LiDAR datasets demonstrate that the method achieves predictive accuracy comparable to centralized models and provides reliable uncertainty quantification.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Sequential Decision Making
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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.

Problem

• Multi-output Gaussian Processes
• large-scale
• distributed
• streaming
• scalability

Innovation

• Consensus-based
• Recursive Gaussian Process
• Multi-output
• Distributed Learning
• Uncertainty Calibration