Sparse non-negative generalized PCA with applications to metabolomics. - Genevera I. Allen; Mirjana Maletic-Savatic (Bioinform., 2011)

A Log-Linear Graphical Model for inferring genetic networks from high-throughput sequencing data. - Genevera I. Allen; Zhandong Liu (BIBM, 2012)

Graphical Models via Generalized Linear Models. - Eunho Yang; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (NIPS, 2012)

Sparse Higher-Order Principal Components Analysis. - Genevera I. Allen (AISTATS, 2012)

Multi-way functional principal components analysis. - Genevera I. Allen (CAMSAP, 2013)

Identifying cancer biomarkers through a network regularized Cox model. - Ying-Wooi Wan; John Nagorski; Genevera I. Allen; Zhaohui Li; Zhandong Liu (GENSiPS, 2013)

Imaging genetics via sparse canonical correlation analysis. - Eric C. Chi; Genevera I. Allen; Hua Zhou 001; Omid Kohannim; Kenneth Lange; Paul M. Thompson (ISBI, 2013)

Conditional Random Fields via Univariate Exponential Families. - Eunho Yang; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (NIPS, 2013)

On Poisson Graphical Models. - Eunho Yang; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (NIPS, 2013)

Local-Aggregate Modeling for Multi-subject Neuroimage Data via Distributed Optimization. - Yue Hu; Genevera I. Allen (PRNI, 2013)

Randomized Approach to Differential Inference in Multi-subject Functional Connectivity. - Manjari Narayan; Genevera I. Allen (PRNI, 2013)

Regularized partial least squares with an application to NMR spectroscopy. - Genevera I. Allen; Christine B. Peterson; Marina Vannucci; Mirjana Maletic-Savatic (Stat. Anal. Data Min., 2013)

Mixed Graphical Models via Exponential Families. - Eunho Yang; Yulia Baker; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (AISTATS, 2014)

Population Inference for Node Level Differences in Multi-subject Functional Connectivity. - Manjari Narayan; Genevera I. Allen (PRNI, 2015)

Graphical models via univariate exponential family distributions. - Eunho Yang; Pradeep Ravikumar; Genevera I. Allen; Zhandong Liu (J. Mach. Learn. Res., 2015)

Comments on "visualizing statistical models": Visualizing modern statistical methods for Big Data. - Genevera I. Allen; Frederick Campbell; Yue Hu (Stat. Anal. Data Min., 2015)

TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. - Ying-Wooi Wan; Genevera I. Allen; Zhandong Liu (Bioinform., 2016)

XMRF: an R package to fit Markov Networks to high-throughput genetics data. - Ying-Wooi Wan; Genevera I. Allen; Yulia Baker; Eunho Yang; Pradeep Ravikumar; Matthew Anderson 002; Zhandong Liu (BMC Syst. Biol., 2016)

The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: from big data to big analytical tools. - Zhandong Liu; W. Jim Zheng; Genevera I. Allen; Yin Liu; Jianhua Ruan; Zhongming Zhao (BMC Bioinform., 2017)

Detecting hidden batch factors through data-adaptive adjustment for biological effects. - Haidong Yi; Ayush T. Raman; Han Zhang 017; Genevera I. Allen; Zhandong Liu (Bioinform., 2018)

Sparse and Functional Principal Components Analysis. - Genevera I. Allen; Michael Weylandt (DSW, 2019)

Graphical Models and Dynamic Latent Factors for Modeling Functional Brain Connectivity. - Andersen Chang; Tianyi Yao; Genevera I. Allen (DSW, 2019)

Clustered Gaussian Graphical Model Via Symmetric Convex Clustering. - Tianyi Yao; Genevera I. Allen (DSW, 2019)

Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization. - Michael Weylandt; John Nagorski; Genevera I. Allen (CoRR, 2019)

Tensor network factorizations: Relationships between brain structural connectomes and traits. - Zhengwu Zhang; Genevera I. Allen; Hongtu Zhu; David B. Dunson (NeuroImage, 2019)

Correlation Imputation in Single cell RNA-seq using Auxiliary Information and Ensemble Learning. - Luqin Gan; Giuseppe Vinci; Genevera I. Allen (BCB, 2020)

Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data. - Kelly Geyer; Frederick Campbell; Andersen Chang; John F. Magnotti; Michael S. Beauchamp; Genevera I. Allen (IEEE BigData, 2020)

Feature Selection for Huge Data via Minipatch Learning. - Tianyi Yao; Genevera I. Allen (CoRR, 2020)

Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering. - Michael Weylandt; T. Mitchell Roddenberry; Genevera I. Allen (CoRR, 2020)

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure. - Xin Tan; Yanwan Dai; Ahmed Imtiaz Humayun; Haoze Chen; Genevera I. Allen; Parag N. Jain (AIME, 2021)

MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling. - Mohammad Taha Toghani; Genevera I. Allen (BigComp, 2021)

Minipatch Learning as Implicit Ridge-Like Regularization. - Tianyi Yao; Daniel LeJeune; Hamid Javadi; Richard G. Baraniuk; Genevera I. Allen (BigComp, 2021)

Experiential Learning in Data Science: Developing an Interdisciplinary, Client-Sponsored Capstone Program. - Genevera I. Allen (SIGCSE, 2021)

Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity. - Minjie Wang; Genevera I. Allen (CoRR, 2021)

Gaussian Graphical Model Selection for Huge Data via Minipatch Learning. - Tianyi Yao; Minjie Wang; Genevera I. Allen (CoRR, 2021)

Network Clustering for Latent State and Changepoint Detection. - Madeline Navarro; Genevera I. Allen; Michael Weylandt (CoRR, 2021)

Integrated Principal Components Analysis. - Tiffany M. Tang; Genevera I. Allen (J. Mach. Learn. Res., 2021)

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data. - Minjie Wang; Genevera I. Allen (J. Mach. Learn. Res., 2021)

Experiential Learning in Data Science Through a Novel Client-Facing Consulting Course. - Arko Barman; Su Chen; Andersen Chang; Genevera I. Allen (FIE, 2022)

Learning Gaussian Graphical Models with Differing Pairwise Sample Sizes. - Lili Zheng; Genevera I. Allen (ICASSP, 2022)

To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier. - Camille Olivia Little; Michael Weylandt; Genevera I. Allen (CoRR, 2022)

Inference for Interpretable Machine Learning: Fast, Model-Agnostic Confidence Intervals for Feature Importance. - Luqin Gan; Lili Zheng; Genevera I. Allen (CoRR, 2022)

Correlation Imputation for Single-Cell RNA-seq. - Luqin Gan; Giuseppe Vinci; Genevera I. Allen (J. Comput. Biol., 2022)

Fast and interpretable consensus clustering via minipatch learning. - Luqin Gan; Genevera I. Allen (PLoS Comput. Biol., 2022)

Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunities. - Genevera I. Allen; Luqin Gan; Lili Zheng (CoRR, 2023)

Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates. - Camille Olivia Little; Debolina Halder Lina; Genevera I. Allen (CoRR, 2023)

SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra. - Xu Han; Wanli Wang; Li-Hua Ma; Ismael Ai-Ramahi; Juan Botas; Kevin Mackenzie; Genevera I. Allen; Damian W. Young; Zhandong Liu; Mirjana Maletic-Savatic (Bioinform., 2023)

Evaluating Individual Contributions in Teams for Project-Based Learning in Data Science. - Arko Barman; Su Chen; Genevera I. Allen (FIE, 2024)

Data Augmentation via Subgroup Mixup for Improving Fairness. - Madeline Navarro; Camille Olivia Little; Genevera I. Allen; Santiago Segarra (ICASSP, 2024)

Addressing Confounds in Functional Connectivity Analyses of Calcium Imaging. - Dingding Ye; Charan Santhirasegaran; Ryan Pai; Genevera I. Allen; Joseph Young (ICASSP, 2024)

Fair Feature Importance Scores for Interpreting Decision Trees. - Camille Olivia Little; Debolina Halder Lina; Genevera I. Allen (Trans. Mach. Learn. Res., 2024)

Fair MP-BOOST: Fair and Interpretable Minipatch Boosting. - Camille Olivia Little; Genevera I. Allen (ICASSP, 2025)

iLOCO: Distribution-Free Inference for Feature Interactions. - Camille Olivia Little; Lili Zheng; Genevera I. Allen (CoRR, 2025)

Are machine learning interpretations reliable? A stability study on global interpretations. - Luqin Gan; Tarek M. Zikry; Genevera I. Allen (CoRR, 2025)

Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices. - Andersen Chang; Tiffany M. Tang; Tarek M. Zikry; Genevera I. Allen (CoRR, 2025)