research
∙
05/29/2021
Learning Graphon Autoencoders for Generative Graph Modeling
Graphon is a nonparametric model that generates graphs with arbitrary si...
share
research
∙
02/04/2021
Hawkes Processes on Graphons
We propose a novel framework for modeling multiple multivariate point pr...
research
∙
12/10/2020
Learning Graphons via Structured Gromov-Wasserstein Barycenters
We propose a novel and principled method to learn a nonparametric graph ...
research
∙
06/04/2020
Hierarchical Optimal Transport for Robust Multi-View Learning
Traditional multi-view learning methods often rely on two assumptions: (...
research
∙
02/07/2020
Learning Autoencoders with Relational Regularization
A new algorithmic framework is proposed for learning autoencoders of dat...
research
∙
10/04/2019
Fused Gromov-Wasserstein Alignment for Hawkes Processes
We propose a novel fused Gromov-Wasserstein alignment method to jointly ...
research
∙
06/20/2019
Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes
We propose a novel adversarial learning strategy for mixture models of H...
research
∙
06/13/2019
Interpretable ICD Code Embeddings with Self- and Mutual-Attention Mechanisms
We propose a novel and interpretable embedding method to represent the i...
research
∙
05/18/2019
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
We propose a scalable Gromov-Wasserstein learning (S-GWL) method and est...
research
∙
01/17/2019
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
A novel Gromov-Wasserstein learning framework is proposed to jointly mat...
research
∙
10/14/2017
Benefits from Superposed Hawkes Processes
The superposition of temporal point processes has been studied for many ...
research
∙
06/28/2017
CASCONet: A Conference dataset
Knowledge mobilization and translation describes the process of moving k...
research
∙
02/22/2017