English(EN) Online Gradient Computation for Warping Gaussian Process Transformations

新的在线方法通过递归梯度计算增强了扭曲高斯过程

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一种新颖的扭曲高斯过程（GPs）在线方法，该方法可以联合更新潜在 GP 矩和扭曲参数。该方法通过实现瞬时负对数似然梯度的精确递归计算，解决了现有流式变体中的局限性。新方法旨在通过一种称为扭曲的参数化变换将非高斯观测映射到潜在的标准 GP 中，从而改进对非高斯观测的处理。 AI

影响这项研究可能导致在机器学习应用中更高效、更准确地对非高斯数据进行建模。

排序理由关于高斯过程新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Emilio Ruiz-Moreno, Konstantinos Slavakis, Baltasar Beferull-Lozano · 2026-09-16 04:00

在线梯度计算用于扭曲高斯过程变换

arXiv:2609.16472v1 Announce Type: new Abstract: Warped Gaussian processes (GPs) handle non-Gaussian observations by mapping them into a latent standard GP via a parametric transformation called warping. Existing streaming variants, however, either optimize the warping parameters …