Consensus-based Recursive Multi-Output Gaussian Process
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913022436442112 |
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| author | Rao, Yogesh Prasanna Kumar Keviczky, Tamas Rajan, Raj Thilak |
| author_facet | Rao, Yogesh Prasanna Kumar Keviczky, Tamas Rajan, Raj Thilak |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10146 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Consensus-based Recursive Multi-Output Gaussian Process Rao, Yogesh Prasanna Kumar Keviczky, Tamas Rajan, Raj Thilak Machine Learning Signal Processing 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. |
| title | Consensus-based Recursive Multi-Output Gaussian Process |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2604.10146 |