Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization

29 March 2025

Papers citing "Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization"

9 / 9 papers shown

Title
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences Siqiao Xue Xiaoming Shi James Y. Zhang Hongyuan Mei AI4TS 36 35 0 04 Oct 2022
Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling Mengfan Yao Siqian Zhao Shaghayegh Sherry Sahebi Reza Feyzi-Behnagh 31 14 0 29 Jan 2021
Generalized Spectral Clustering via Gromov-Wasserstein Learning Samir Chowdhury Tom Needham 33 54 0 07 Jun 2020
Transformer Hawkes Process Simiao Zuo Haoming Jiang Zichong Li T. Zhao H. Zha AI4TS 63 292 0 21 Feb 2020
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching Hongteng Xu Dixin Luo Lawrence Carin 55 194 0 18 May 2019
The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks Ruthwik R. Junuthula Maysam Haghdan Kevin S. Xu Vijay Devabhaktuni 131 31 0 29 Nov 2017
THAP: A Matlab Toolkit for Learning with Hawkes Processes Hongteng Xu H. Zha 28 13 0 28 Aug 2017
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution Mehrdad Farajtabar Yichen Wang Manuel Gomez Rodriguez Shuang Li H. Zha Le Song 65 239 0 08 Jul 2015
Discovering Latent Network Structure in Point Process Data Scott W. Linderman Ryan P. Adams 74 282 0 04 Feb 2014