1 citations · 1 across the 6 of their papers we have counts for
7 papers
Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
Andersen Chang, Tiffany M. Tang, Tarek M. Zikry +1
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy,…
Cluster Quilting: Spectral Clustering for Patchwork Learning
Lili Zheng, Andersen Chang, Genevera I. Allen
Patchwork learning arises as a new and challenging data collection paradigm where both samples and features are observed in fragmented subsets. Due to technological limits, measure…
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…
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…
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data
Kelly Geyer, Frederick Campbell, Andersen Chang +3
ElectroCOrticoGraphy (ECoG) technology measures electrical activity in the human brain via electrodes placed directly on the cortical surface during neurosurgery. Through its capab…
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…