Online Gradient Computation for Warping Gaussian Process Transformations

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.

Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2609.16472 [cs.LG]

(or arXiv:2609.16472v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.16472 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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