← Back to all news
arXiv Machine Learning By Kaichuang Yang, H{\aa}vard Rue, Jakob Zeitler

ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach

Read the original on arXiv Machine Learning →

The paper introduces ISBO, a scalable Spatio‑Temporal Bayesian Optimization framework that models log‑intensity using a Log‑Gaussian Cox Process and performs inference via the INLA‑SPDE approach. By employing a Matern field on meshes, ISBO obtains a sparse Gaussian Markov Random Field, enabling fast and accurate posterior inference during sequential optimization. Experiments on synthetic and real‑world datasets demonstrate accurate peak discovery, intensity recovery, and significant speedups compared to an RKHS‑based baseline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 8

Scalable Logistic Gaussian Process Density Regression with Kinetic Langevin Sampling

The paper introduces a scalable Bayesian method for conditional density estimation using a logistic Gaussian process. It models the log conditional density with a separable covariance— a Matérn kernel on the response dimension and a Nyström-feature kernel on covariates— and samples the latent field via kinetic Langevin dynamics instead of traditional approximations. Experiments on photometric‑redshift data with up to 3.9 million observations show the approach matches state‑of‑the‑art tabular foundation models on density and calibration metrics.

By Daniel Paulin, \'Ad\'am Jung, Andr\'as A. Bencz\'ur
benchmarkssafety
More like this →