21 citations · 34 across the 6 of their papers we have counts for
6 papers
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…
Multiscale Comparative Connectomics
Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford +8
The connectome, a map of the structural and/or functional connections in the brain, provides a complex representation of the neurobiological phenotypes on which it supervenes. This…
Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation with Applications in Connectomics
Jaewon Chung, Bijan Varjavand, Jesus Arroyo +5
Testing whether two graphs come from the same distribution is of interest in many real world scenarios, including brain network analysis. Under the random dot product graph model,…
Independence Testing for Temporal Data
Cencheng Shen, Jaewon Chung, Ronak Mehta +2
Temporal data are increasingly prevalent in modern data science. A fundamental question is whether two time series are related or not. Existing approaches often have limitations, s…
GraSPy: Graph Statistics in Python
Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford +3
We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible a…
Sparse Projection Oblique Randomer Forests
Tyler M. Tomita, James Browne, Cencheng Shen +8
Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision…