86 citations · 467 across the 40 of their papers we have counts for
42 papers
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…
Data Augmentation via Subgroup Mixup for Improving Fairness
Madeline Navarro, Camille Little, Genevera I. Allen +1
In this work, we propose data augmentation via pairwise mixup across subgroups to improve group fairness. Many real-world applications of machine learning systems exhibit biases ac…
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities
Genevera I. Allen, Luqin Gan, Lili Zheng
New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only p…
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…
LOCO Feature Importance Inference without Data Splitting via Minipatch Ensembles
Luqin Gan, Lili Zheng, Genevera I. Allen
Feature importance inference is critical for the interpretability and reliability of machine learning models. There has been increasing interest in developing model-agnostic approa…
To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier
Camille Olivia Little, Michael Weylandt, Genevera I Allen
Algorithmic fairness has emerged as an important consideration when using machine learning to make high-stakes societal decisions. Yet, improved fairness often comes at the expense…
Network Clustering for Latent State and Changepoint Detection
Madeline Navarro, Genevera I. Allen, Michael Weylandt
Network models provide a powerful and flexible framework for analyzing a wide range of structured data sources. In many situations of interest, however, multiple networks can be co…
Fast and Interpretable Consensus Clustering via Minipatch Learning
Luqin Gan, Genevera I. Allen
Consensus clustering has been widely used in bioinformatics and other applications to improve the accuracy, stability and reliability of clustering results. This approach ensembles…
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles
Tianyi Yao, Minjie Wang, Genevera I. Allen
Gaussian graphical models provide a powerful framework for uncovering conditional dependence relationships between sets of nodes; they have found applications in a wide variety of…
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…
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity
Minjie Wang, Genevera I. Allen
In neuroscience, researchers seek to uncover the connectivity of neurons from large-scale neural recordings or imaging; often people employ graphical model selection and estimation…
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…
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
Michael Weylandt, T. Mitchell Roddenberry, Genevera I. Allen
Clustering is a ubiquitous problem in data science and signal processing. In many applications where we observe noisy signals, it is common practice to first denoise the data, perh…
MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
Mohammad Taha Toghani, Genevera I. Allen
Boosting methods are among the best general-purpose and off-the-shelf machine learning approaches, gaining widespread popularity. In this paper, we seek to develop a boosting metho…
Feature Selection for Huge Data via Minipatch Learning
Tianyi Yao, Genevera I. Allen
Feature selection often leads to increased model interpretability, faster computation, and improved model performance by discarding irrelevant or redundant features. While feature…
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…
Graph quilting: graphical model selection from partially observed covariances
Giuseppe Vinci, Gautam Dasarathy, Genevera I. Allen
Graphical model selection is a seemingly impossible task when many pairs of variables are never jointly observed; this requires inference of conditional dependencies with no observ…
Clustered Gaussian Graphical Model via Symmetric Convex Clustering
Tianyi Yao, Genevera I. Allen
Knowledge of functional groupings of neurons can shed light on structures of neural circuits and is valuable in many types of neuroimaging studies. However, accurately determining…
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…
Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization
Michael Weylandt, John Nagorski, Genevera I. Allen
Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations. Despi…