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09/13/2023
Data Augmentation via Subgroup Mixup for Improving Fairness
In this work, we propose data augmentation via pairwise mixup across sub...
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08/02/2023
Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunities
New technologies have led to vast troves of large and complex datasets a...
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05/22/2023
Nonparanormal Graph Quilting with Applications to Calcium Imaging
Probabilistic graphical models have become an important unsupervised lea...
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10/20/2022
Graphical Model Inference with Erosely Measured Data
In this paper, we investigate the Gaussian graphical model inference pro...
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09/17/2022
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity
As a tool for estimating networks in high dimensions, graphical models a...
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06/05/2022
Inference for Interpretable Machine Learning: Fast, Model-Agnostic Confidence Intervals for Feature Importance
In order to trust machine learning for high-stakes problems, we need mod...
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05/31/2022
To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier
Algorithmic fairness has emerged as an important consideration when usin...
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11/01/2021
Network Clustering for Latent State and Changepoint Detection
Network models provide a powerful and flexible framework for analyzing a...
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10/22/2021
Gaussian Graphical Model Selection for Huge Data via Minipatch Learning
Gaussian graphical models are essential unsupervised learning techniques...
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10/05/2021
Fast and Interpretable Consensus Clustering via Minipatch Learning
Consensus clustering has been widely used in bioinformatics and other ap...
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06/22/2021
Extreme Graphical Models with Applications to Functional Neuronal Connectivity
With modern calcium imaging technology, the activities of thousands of n...
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04/13/2021
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity
In neuroscience, researchers seek to uncover the connectivity of neurons...
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12/08/2020
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
Clustering is a ubiquitous problem in data science and signal processing...
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11/15/2020
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data
ElectroCOrticoGraphy (ECoG) technology measures electrical activity in t...
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11/14/2020
MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
Boosting methods are among the best general-purpose and off-the-shelf ma...
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10/16/2020
Feature Selection for Huge Data via Minipatch Learning
Feature selection often leads to increased model interpretability, faste...
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05/25/2020
Supervised Convex Clustering
Clustering has long been a popular unsupervised learning approach to ide...
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12/11/2019
Graph quilting: graphical model selection from partially observed covariances
We investigate the problem of conditional dependence graph estimation wh...
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12/11/2019
Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
In mixed multi-view data, multiple sets of diverse features are measured...
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05/30/2019
Clustered Gaussian Graphical Model via Symmetric Convex Clustering
Knowledge of functional groupings of neurons can shed light on structure...
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03/27/2019
Feature Selection for Data Integration with Mixed Multi-view Data
Data integration methods that analyze multiple sources of data simultane...
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01/06/2019
Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization
Convex clustering is a promising new approach to the classical problem o...
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01/06/2019
Dynamic Visualization and Fast Computation for Convex Clustering and Bi-Clustering
Convex clustering is a promising new approach to the classical problem o...
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10/01/2018
Integrated Principal Components Analysis
Data integration, or the strategic analysis of multiple sources of data ...
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06/07/2018
Tensor network factorizations: Relationships between brain structural connectomes and traits
Advanced brain imaging techniques make it possible to measure individual...
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08/31/2016
A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution
The Poisson distribution has been widely studied and used for modeling u...
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11/02/2014
A General Framework for Mixed Graphical Models
"Mixed Data" comprising a large number of heterogeneous variables (e.g. ...
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08/05/2014
Convex Biclustering
In the biclustering problem, we seek to simultaneously group observation...
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09/11/2013
Sparse and Functional Principal Components Analysis
Regularized principal components analysis, especially Sparse PCA and Fun...
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01/17/2013
On Graphical Models via Univariate Exponential Family Distributions
Undirected graphical models, or Markov networks, are a popular class of ...
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04/17/2012
Regularized Partial Least Squares with an Application to NMR Spectroscopy
High-dimensional data common in genomics, proteomics, and chemometrics o...
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02/11/2012
Regularized Tensor Factorizations and Higher-Order Principal Components Analysis
High-dimensional tensors or multi-way data are becoming prevalent in are...
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02/15/2011