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arXiv Machine Learning By Thomas Cowperthwaite, Louis Sharrock, Lachlan Astfalck, Henry Moss

Feature Space Adaptation for Effortless Gaussian Process Flows

Read the original on arXiv Machine Learning →

The paper introduces improvements to FlowGP, a method for conditional sampling from Gaussian processes outside the linear-Gaussian regime. It adds kernel approximations to scale to high-resolution domains and a new technique for computing marginal likelihood by measuring the work required to steer diffusion toward conditioning statements. These advances enable hyperparameter optimisation within FlowGP and are demonstrated on tasks such as probabilistic downscaling, PDE solution inference on irregular domains, and sea level anomaly field recovery from non-Gaussian satellite data.

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