Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes

YIXUAN ZHANG · Quyu Kong · Feng Zhou

Great Hall & Hall B1+B2 (level 1) #1013
[ ]
Wed 13 Dec 3 p.m. PST — 5 p.m. PST


In this study, we propose a novel deep spatio-temporal point process model, Deep Kernel Mixture Point Processes (DKMPP), that incorporates multimodal covariate information. DKMPP is an enhanced version of Deep Mixture Point Processes (DMPP), which uses a more flexible deep kernel to model complex relationships between events and covariate data, improving the model's expressiveness. To address the intractable training procedure of DKMPP due to the non-integrable deep kernel, we utilize an integration-free method based on score matching, and further improve efficiency by adopting a scalable denoising score matching method. Our experiments demonstrate that DKMPP and its corresponding score-based estimators outperform baseline models, showcasing the advantages of incorporating covariate information, utilizing a deep kernel, and employing score-based estimators.

Chat is not available.