88 citations · 197 across the 18 of their papers we have counts for
5 papers · 1 filter
Fused Gromov-Wasserstein Alignment for Hawkes Processes
Dixin Luo, Hongteng Xu, Lawrence Carin
We propose a novel fused Gromov-Wasserstein alignment method to jointly learn the Hawkes processes in different event spaces, and align their event types. Given two Hawkes processe…
Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes
Dixin Luo, Hongteng Xu, Lawrence Carin
We propose a novel adversarial learning strategy for mixture models of Hawkes processes, leveraging data augmentation techniques of Hawkes process in the framework of self-paced le…
Interpretable ICD Code Embeddings with Self- and Mutual-Attention Mechanisms
Dixin Luo, Hongteng Xu, Lawrence Carin
We propose a novel and interpretable embedding method to represent the international statistical classification codes of diseases and related health problems (i.e., ICD codes). Thi…
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Hongteng Xu, Dixin Luo, Lawrence Carin
We propose a scalable Gromov-Wasserstein learning (S-GWL) method and establish a novel and theoretically-supported paradigm for large-scale graph analysis. The proposed method is b…
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Hongteng Xu, Dixin Luo, Hongyuan Zha +1
A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes. Using Gromov-Wasserstein discr…