Online Gradient Computation for Warping Gaussian Process Transformations

• URL: http://arxiv.org/abs/2609.16472v1
• Date: Tue, 15 Sep 2026 00:44:38 GMT
• Title: Online Gradient Computation for Warping Gaussian Process Transformations
• Abstract summary: Warped Gaussian processes (GPs) handle non-Gaussian observations by mapping them into a latent standard GP via a parametric transformation called warping.<n>Existing streaming variants, however, either optimize the warping parameters periodically or sacrifice analytical tractability for a higher model capacity.<n>We propose a novel online method for warped GPs that jointly updates the latent GP moments and optimize the warping parameters.
• Score: 10.695203913811314
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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