86 citations · 467 across the 40 of their papers we have counts for
17 papers · 1 filter
Joint Semi-Symmetric Tensor PCA for Integrating Multi-modal Populations of Networks
Jiaming Liu, Lili Zheng, Zhengwu Zhang +1
Multi-modal populations of networks arise in many scenarios including in large-scale multi-modal neuroimaging studies that capture both functional and structural neuroimaging data…
Nonparanormal Graph Quilting with Applications to Calcium Imaging
Andersen Chang, Lili Zheng, Gautam Dasarthy +1
Probabilistic graphical models have become an important unsupervised learning tool for detecting network structures for a variety of problems, including the estimation of functiona…
Graphical Model Inference with Erosely Measured Data
Lili Zheng, Genevera I. Allen
In this paper, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data.…
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity
Andersen Chang, Lili Zheng, Genevera I. Allen
As a tool for estimating networks in high dimensions, graphical models are commonly applied to calcium imaging data to estimate functional neuronal connectivity, i.e. relationships…
Subbotin Graphical Models for Extreme Value Dependencies with Applications to Functional Neuronal Connectivity
Andersen Chang, Genevera I. Allen
With modern calcium imaging technology, activities of thousands of neurons can be recorded in vivo. These experiments can potentially provide new insights into intrinsic functional…
Sparse Regression for Extreme Values
Andersen Chang, Minjie Wang, Genevera Allen
We study the problem of selecting features associated with extreme values in high dimensional linear regression. Normally, in linear modeling problems, the presence of abnormal ext…
Supervised Convex Clustering
Minjie Wang, Tianyi Yao, Genevera I. Allen
Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications. Yet, coming…
Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
Minjie Wang, Genevera I. Allen
In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group str…
Feature Selection for Data Integration with Mixed Multi-view Data
Yulia Baker, Tiffany M. Tang, Genevera I. Allen
Data integration methods that analyze multiple sources of data simultaneously can often provide more holistic insights than can separate inquiries of each data source. Motivated by…
Integrated Principal Components Analysis
Tiffany M. Tang, Genevera I. Allen
Data integration, or the strategic analysis of multiple sources of data simultaneously, can often lead to discoveries that may be hidden in individualistic analyses of a single dat…
A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution
David I. Inouye, Eunho Yang, Genevera I. Allen +1
The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependenci…
Within Group Variable Selection through the Exclusive Lasso
Frederick Campbell, Genevera I. Allen
Many data sets consist of variables with an inherent group structure. The problem of group selection has been well studied, but in this paper, we seek to do the opposite: our goal…
ADMM Algorithmic Regularization Paths for Sparse Statistical Machine Learning
Yue Hu, Eric Chi, Genevera I. Allen
Optimization approaches based on operator splitting are becoming popular for solving sparsity regularized statistical machine learning models. While many have proposed fast algorit…
Two Sample Inference for Populations of Graphical Models with Applications to Functional Connectivity
Manjari Narayan, Genevera I. Allen, Steffie Tomson
Gaussian Graphical Models (GGM) are popularly used in neuroimaging studies based on fMRI, EEG or MEG to estimate functional connectivity, or relationships between remote brain regi…
Convex Biclustering
Eric C. Chi, Genevera I. Allen, Richard G. Baraniuk
In the biclustering problem, we seek to simultaneously group observations and features. While biclustering has applications in a wide array of domains, ranging from text mining to…
Local-Aggregate Modeling for Big-Data via Distributed Optimization: Applications to Neuroimaging
Yue Hu, Genevera I. Allen
Technological advances have led to a proliferation of structured big data that have matrix-valued covariates. We are specifically motivated to build predictive models for multi-sub…
A Generalized Least Squares Matrix Decomposition
Genevera I. Allen, Logan Grosenick, Jonathan Taylor
Variables in many massive high-dimensional data sets are structured, arising for example from measurements on a regular grid as in imaging and time series or from spatial-temporal…