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New Adaptive Nyström Method Enhances Gaussian Process Regression Scalability

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an adaptive Nyström method to improve the scalability of Gaussian Process Regression (GPR). This new approach greedily selects landmark points to minimize approximation errors, outperforming random selection in accuracy and stability. The method achieves performance comparable to exact GPR while scaling linearly with sample size, making it suitable for large-scale computer experiments. AI

IMPACT Enhances scalability for uncertainty quantification in large-scale machine learning experiments.

RANK_REASON The cluster contains a research paper detailing a new methodology for Gaussian Process Regression. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Adaptive Nyström Method Enhances Gaussian Process Regression Scalability

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The cluster contains a research paper detailing a new methodology for Gaussian Process Regression. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Lulu Kang ·

    Adaptive Nystr\"om for Gaussian Process Regression

    arXiv:2607.27427v1 Announce Type: cross Abstract: Gaussian Process Regression (GPR) is a robust framework for uncertainty quantification, yet its $O(n^3)$ complexity limits its scalability. Low-rank Nystr\"om approximations can reduce this burden to $O(nm^2)$, but their accuracy …

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