Nonparametric Intensity Estimation for Covariate-Driven Poisson Processes Using Deep Learning
Paper recorded by Signals 4 on 2026-11-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03
Category: cs.LG · 机器学习 · first seen 2026-11-03
Abstract
We consider nonparametric maximum likelihood estimation of the intensity function of a Poisson process, allowing the intensity to depend on observed covariates. Such models describe how event rates vary with external conditions, including conditions under which the intensity becomes arbitrarily small or vanishes. However, risk analysis based on the Kullback-Leibler (KL) divergence typically requir
Read on arXiv →
Cite this page: Nonparametric Intensity Estimation for Covariate-Driven Poisson Processes Using Deep Learning: the #27 most recent of 703 cs.LG papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/nonparametric-intensity-estimation-for-covariate-driven-poisson-processes-using-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable:
papers.json
Get 4 AI signals a day by email — free.
Get 4 AI signals a day by email — free