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GraphGP algorithm scales Gaussian processes to billion parameters

By PulseAugur Editorial · [2 sources] ·

Researchers have developed GraphGP, a GPU-accelerated algorithm designed to make Gaussian processes more scalable. This new method utilizes Vecchia's approximation to reduce the computational complexity from cubic to linear, enabling the handling of nearly a billion parameters. Key innovations include a novel bit-reversed k-d tree ordering for efficient neighbor searches and parallel processing, alongside a differentiable CUDA implementation that significantly outperforms existing JAX baselines in speed and memory usage. AI

IMPACT Enables larger-scale applications of Gaussian processes in machine learning and scientific modeling.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its implementation.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Benjamin Dodge, Philipp Frank, Susan E. Clark ·

    GraphGP: Scalable Gaussian Processes with Vecchia's Approximation

    arXiv:2606.11402v1 Announce Type: cross Abstract: Gaussian processes are a powerful tool for modeling continuous fields, but their naive $\mathcal{O}(N^3)$ computational cost and $\mathcal{O}(N^2)$ memory requirement often limit their practical use. Vecchia's approximation is a s…

  2. arXiv stat.ML TIER_1 English(EN) · Susan E. Clark ·

    GraphGP: Scalable Gaussian Processes with Vecchia's Approximation

    Gaussian processes are a powerful tool for modeling continuous fields, but their naive $\mathcal{O}(N^3)$ computational cost and $\mathcal{O}(N^2)$ memory requirement often limit their practical use. Vecchia's approximation is a sparse precision matrix approximation for stationar…

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