New Hilbert Space Gaussian Process method speeds up sequential design

Researchers have developed a new Hilbert space Gaussian process approximation to improve sequential design in expensive simulation experiments. This novel approach allows for closed-form evaluation of the integrated mean squared error acquisition function, which was previously a computational bottleneck. The method demonstrates significantly reduced prediction error and faster computation times compared to existing benchmarks, offering a more accurate and efficient solution for Gaussian process-based sequential design. AI

影响 Introduces a more efficient and accurate method for Gaussian process-based sequential design, potentially impacting simulation experiments and optimization tasks.

排序理由 This is a research paper detailing a novel approximation method for Gaussian processes.

在 arXiv stat.ML 阅读 →

• Hilbert space Gaussian process
• IMSE acquisition function
• Gaussian processes

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