发表机构
Simula Metropolitan Center for Digital Engineering; Simula Research Laboratory; Institute of Science Tokyo（Simula城市数字工程中心; Simula研究实验室; 东京科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对扭曲高斯过程流式处理中参数优化与解析可处理性的权衡，提出基于精确递归梯度计算的在线方法，联合更新潜在GP矩与扭曲参数。
AI 中文摘要
扭曲高斯过程（Warped Gaussian Processes, GPs）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高斯过程中，从而处理非高斯观测。然而，现有的流式变体要么周期性地优化扭曲参数，要么为了更高的模型容量而牺牲解析可处理性。为了弥合这一差距，我们证明了扭曲高斯过程的瞬时负对数似然的梯度可以进行精确的递归计算。基于这一结果，我们提出了一种新颖的在线方法用于扭曲高斯过程，该方法联合更新潜在高斯过程的矩并优化扭曲参数。
英文摘要
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 periodically or sacrifice analytical tractability for a higher model capacity. To bridge this gap, we show that the gradient of the instantaneous negative log-likelihood of a warped GP admits an exact recursive computation. Based on this result, we propose a novel online method for warped GPs that jointly updates the latent GP moments and optimizes the warping parameters.
