88 citations · 102 across the 13 of their papers we have counts for
8 papers · 1 filter
Learning Graphon Autoencoders for Generative Graph Modeling
Hongteng Xu, Peilin Zhao, Junzhou Huang +1
Graphon is a nonparametric model that generates graphs with arbitrary sizes and can be induced from graphs easily. Based on this model, we propose a novel algorithmic framework cal…
Hawkes Processes on Graphons
Hongteng Xu, Dixin Luo, Hongyuan Zha
We propose a novel framework for modeling multiple multivariate point processes, each with heterogeneous event types that share an underlying space and obey the same generative mec…
Learning Graphons via Structured Gromov-Wasserstein Barycenters
Hongteng Xu, Dixin Luo, Lawrence Carin +1
We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs…
Hierarchical Optimal Transport for Robust Multi-View Learning
Dixin Luo, Hongteng Xu, Lawrence Carin
Traditional multi-view learning methods often rely on two assumptions: () the samples in different views are well-aligned, and () their representations in latent space obey…
Learning Autoencoders with Relational Regularization
Hongteng Xu, Dixin Luo, Ricardo Henao +2
A new algorithmic framework is proposed for learning autoencoders of data distributions. We minimize the discrepancy between the model and target distributions, with a \emph{relati…
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…