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#machine learning Preprint Oct 2026

Scalable Logistic Gaussian Process Density Regression with Kinetic Langevin Sampling

Conditional density estimation targets the full distribution of a response given covariates, as required, for example, for per-galaxy photometric redshifts. We develop a scalable Bayesian estimator based on the logistic Gaussian process. The log conditional density has a separable covariance: a Mat\'ern kernel along th...

Daniel Paulin, Á. Jung, András A. Benczúr · 0 citations

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