89 citations · 430 across the 37 of their papers we have counts for
4 papers · 1 filter
Learning sources of variability from high-dimensional observational studies
Eric W. Bridgeford, Jaewon Chung, Brian Gilbert +6
Causal inference studies whether the presence of a variable influences an observed outcome. As measured by quantities such as the "average treatment effect," this paradigm is emplo…
Community Correlations and Testing Independence Between Binary Graphs
Cencheng Shen, Jesüs Arroyo, Junhao Xiong +1
Graph data has a unique structure that deviates from standard data assumptions, often necessitating modifications to existing methods or the development of new ones to ensure valid…
Discovering the Signal Subgraph: An Iterative Screening Approach on Graphs
Cencheng Shen, Shangsi Wang, Alexandra Badea +2
Supervised learning on graphs is a challenging task due to the high dimensionality and inherent structural dependencies in the data, where each edge depends on a pair of vertices.…
Network Dependence Testing via Diffusion Maps and Distance-Based Correlations
Youjin Lee, Cencheng Shen, Carey E. Priebe +1
Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high-dimensionality of n…