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arXiv Machine Learning By Josephine King, Ali Emre Balci, Raj Thilak Rajan

Resource-Efficient Distributed Recursive Gaussian Processes

Read the original on arXiv Machine Learning →

The paper introduces two distributed recursive Gaussian process algorithms, ADMM‑RGP and PDMM‑RGP, designed for multi‑output regression in multi‑agent systems. It analyzes their stability and convergence, proposes parameter selection strategies to speed up convergence, and demonstrates that the methods reduce communication overhead while preserving estimation accuracy and consensus. Experiments on a real‑world wind dataset confirm the algorithms’ effectiveness across different communication graph connectivities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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