Online Gradient Computation for Warping Gaussian Process Transformations

2026-09-14

AI Summary

本文提出一种在线方法，通过递归计算变形高斯过程的瞬时负对数似然梯度，同时更新潜在GP矩和优化变形参数，解决了非高斯观测问题。

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 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.

Problem

• warping
• Gaussian process
• online gradient computation
• latent GP moments

Innovation

• Online Gradient Computation
• Warping Parameters Optimization
• Latent GP Moments