activity
20192021
most citedPrincipled Hyperedge Prediction with Structural Spectral Features and Neural Networks

10 citations · 10 across the 2 of their papers we have counts for

collaborators

5 papers

stat.ME2021

Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence

Wennan Chang, Pengtao Dang, Changlin Wan +7

In this paper, we propose a Spatial Robust Mixture Regression model to investigate the relationship between a response variable and a set of explanatory variables over the spatial…

cs.SI202110 cited

Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks

Changlin Wan, Muhan Zhang, Wei Hao +3

Hypergraph offers a framework to depict the multilateral relationships in real-world complex data. Predicting higher-order relationships, i.e hyperedge, becomes a fundamental probl…

cs.LG2020

Geometric All-Way Boolean Tensor Decomposition

Changlin Wan, Wennan Chang, Tong Zhao +2

Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains. Boolean Tensor Decomposition…

stat.ME2020

Component-wise Adaptive Trimming For Robust Mixture Regression

Wennan Chang, Xinyu Zhou, Yong Zang +2

Parameter estimation of mixture regression model using the expectation maximization (EM) algorithm is highly sensitive to outliers. Here we propose a fast and efficient robust mixt…

cs.LG2019

Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation

Changlin Wan, Wennan Chang, Tong Zhao +3

Boolean matrix has been used to represent digital information in many fields, including bank transaction, crime records, natural language processing, protein-protein interaction, e…