English(EN) Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

新方法为核回归提供严格的不确定性界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员开发了一种在新方法，用于在再生核希尔伯特空间中计算多元函数的严格、确定性不确定性界限。该方法旨在处理有界噪声条件，并且可以轻松集成到现有的高斯过程置信界限框架中。新方法推广了先前结果，并通过一个与四旋翼飞行器学习动力学相关的示例进行了演示。 AI

影响为机器学习中的不确定性量化提供了一种更稳健的方法，这对于安全控制应用至关重要。

排序理由该集群包含一篇学术论文，详细介绍了一种用于核回归不确定性界限的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Amon Lahr, Anna Scampicchio, Johannes K\"ohler, Melanie N. Zeilinger · 2026-05-26 04:00

有界噪声下多元核回归的最优不确定性界限：基于高斯过程的对偶函数

arXiv:2603.16481v2 Announce Type: replace Abstract: Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control. In this domain, kernel methods such as Gaussian p…