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New online method enhances warped Gaussian processes with recursive gradient computation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel online method for warped Gaussian processes (GPs) that allows for the joint update of latent GP moments and warping parameters. This approach addresses limitations in existing streaming variants by enabling exact recursive computation of the gradient of the instantaneous negative log-likelihood. The new method aims to improve the handling of non-Gaussian observations by mapping them into a latent standard GP through a parametric transformation called warping. AI

IMPACT This research could lead to more efficient and accurate modeling of non-Gaussian data in machine learning applications.

RANK_REASON Academic paper on a novel method for Gaussian Processes. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New online method enhances warped Gaussian processes with recursive gradient computation

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Academic paper on a novel method for Gaussian Processes. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Emilio Ruiz-Moreno, Konstantinos Slavakis, Baltasar Beferull-Lozano ·

    Online Gradient Computation for Warping Gaussian Process Transformations

    arXiv:2609.16472v1 Announce Type: new 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 …

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