504 citations · 2k across the 109 of their papers we have counts for
14 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…
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
Guodong Chen, Jesús Arroyo, Avanti Athreya +7
This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anom…
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,…
Inference for multiple heterogeneous networks with a common invariant subspace
Jesús Arroyo, Avanti Athreya, Joshua Cape +3
The development of models for multiple heterogeneous network data is of critical importance both in statistical network theory and across multiple application domains. Although sin…
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.…
Statistical inference on random dot product graphs: a survey
Avanti Athreya, Donniell E. Fishkind, Keith Levin +7
The random dot product graph (RDPG) is an independent-edge random graph that is analytically tractable and, simultaneously, either encompasses or can successfully approximate a wid…
Robust Estimation from Multiple Graphs under Gross Error Contamination
Runze Tang, Minh Tang, Joshua T. Vogelstein +1
Estimation of graph parameters based on a collection of graphs is essential for a wide range of graph inference tasks. In practice, weighted graphs are generally observed with edge…
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…
Connectome Smoothing via Low-rank Approximations
Runze Tang, Michael Ketcha, Alexandra Badea +5
In statistical connectomics, the quantitative study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem. Often, this problem is esp…
An M-Estimator for Reduced-Rank High-Dimensional Linear Dynamical System Identification
Shaojie Chen, Kai Liu, Yuguang Yang +5
High-dimensional time-series data are becoming increasingly abundant across a wide variety of domains, spanning economics, neuroscience, particle physics, and cosmology. Fitting st…
Nonparametric Bayes Modeling of Populations of Networks
Daniele Durante, David B. Dunson, Joshua T. Vogelstein
Replicated network data are increasingly available in many research fields. In connectomic applications, inter-connections among brain regions are collected for each patient under…
Parallel inversion of huge covariance matrices
Anjishnu Banerjee, Joshua Vogelstein, David Dunson
An extremely common bottleneck encountered in statistical learning algorithms is inversion of huge covariance matrices, examples being in evaluating Gaussian likelihoods for a larg…
Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown
Donniell E. Fishkind, Daniel L. Sussman, Minh Tang +2
For random graphs distributed according to a stochastic block model, we consider the inferential task of partioning vertices into blocks using spectral techniques. Spectral partion…