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 Matérn kernel along the response, represented in a truncated Fourier basis on a circle, and a covariate kernel represented by Nyström features, which accommodate non-stationary kernels with input-dependent amplitudes and length scales. Instead of a Laplace or variational approximation, we sample the latent field of this finite-feature model. Given the hyperparameters, its posterior is strongly log-...
| Authors | Daniel Paulin, Ádám Jung, András A. Benczúr |
|---|---|
| Primary category | stat.ML |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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