Online Gradient Computation for Warping Gaussian Process Transformations
cs.LG, eess.SP
Submitted: 2026-09-15
Updated: 2026-09-15
Code: https://github.com/SIGIPRO/onlineWGP
License: http://creativecommons.org/licenses/by/4.0/
The gist: Warped Gaussian processes (GPs) handle non-Gaussian observations by mapping them into a latent standard GP via a parametric transformation called warping.
Terminology
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.
Sources
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