Dixin Luo | DeepAI

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Featured Co-authors

Lawrence Carin
178 publications
Xu Chen
155 publications
Junzhou Huang
106 publications
Hongyuan Zha
98 publications
Peilin Zhao
77 publications
Ricardo Henao
50 publications
Hongteng Xu
41 publications
Kelly Lyons
3 publications
Svati Shah
1 publication

∙ 05/29/2021

Learning Graphon Autoencoders for Generative Graph Modeling

Graphon is a nonparametric model that generates graphs with arbitrary si...

0 Hongteng Xu, et al. ∙

∙ 02/04/2021

Hawkes Processes on Graphons

We propose a novel framework for modeling multiple multivariate point pr...

0 Hongteng Xu, et al. ∙

∙ 12/10/2020

Learning Graphons via Structured Gromov-Wasserstein Barycenters

We propose a novel and principled method to learn a nonparametric graph ...

0 Hongteng Xu, et al. ∙

∙ 06/04/2020

Hierarchical Optimal Transport for Robust Multi-View Learning

Traditional multi-view learning methods often rely on two assumptions: (...

0 Dixin Luo, et al. ∙

∙ 02/07/2020

Learning Autoencoders with Relational Regularization

A new algorithmic framework is proposed for learning autoencoders of dat...

0 Hongteng Xu, et al. ∙

∙ 10/04/2019

Fused Gromov-Wasserstein Alignment for Hawkes Processes

We propose a novel fused Gromov-Wasserstein alignment method to jointly ...

0 Dixin Luo, et al. ∙

∙ 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...

0 Dixin Luo, et al. ∙

∙ 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...

0 Dixin Luo, et al. ∙

∙ 05/18/2019

Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

We propose a scalable Gromov-Wasserstein learning (S-GWL) method and est...

0 Hongteng Xu, et al. ∙

∙ 01/17/2019

Gromov-Wasserstein Learning for Graph Matching and Node Embedding

A novel Gromov-Wasserstein learning framework is proposed to jointly mat...

12 Hongteng Xu, et al. ∙

∙ 10/14/2017

Benefits from Superposed Hawkes Processes

The superposition of temporal point processes has been studied for many ...

0 Hongteng Xu, et al. ∙

∙ 06/28/2017

CASCONet: A Conference dataset

Knowledge mobilization and translation describes the process of moving k...

0 Dixin Luo, et al. ∙

∙ 02/22/2017

Learning Hawkes Processes from Short Doubly-Censored Event Sequences

Many real-world applications require robust algorithms to learn point pr...

0 Hongteng Xu, et al. ∙

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