Reproduce “ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Kaichuang Yang, Håvard Rue, Jakob Zeitler
ISBO searches for high-intensity regions in point processes by revealing events within local windows rather than receiving scalar scores. It fits a log-Gaussian Cox process using INLA and an SPDE mesh, updating estimates of latent intensity and uncertainty. On a known-ground-truth synthetic test, average region-based simple regret reaches zero by iterations 14–15; this does not guarantee performance on real-world problems.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.