
Multiscale graph principal component analysis for connectomics
In brain connectomics, the cortical surface is parcellated into differen...
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Bayesian neural networks and dimensionality reduction
In conducting nonlinear dimensionality reduction and feature learning, ...
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Principal Ellipsoid Analysis (PEA): Efficient nonlinear dimension reduction clustering
Even with the rise in popularity of overparameterized models, simple di...
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Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity
Factor models are routinely used for dimensionality reduction in modelin...
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Posterior computation with the Gibbs zigzag sampler
Markov chain Monte Carlo (MCMC) sampling algorithms have dominated the l...
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Fiedler Regularization: Learning Neural Networks with Graph Sparsity
We introduce a novel regularization approach for deep learning that inco...
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Bayesian joint modeling of chemical structure and dose response curves
Today there are approximately 85,000 chemicals regulated under the Toxic...
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Predicting Phenotypes from Brain Connection Structure
This article focuses on the problem of predicting a response variable ba...
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Bayesian Hierarchical Factor Regression Models to Infer Cause of Death From Verbal Autopsy Data
In lowresource settings where vital registration of death is not routin...
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Efficient posterior sampling for highdimensional imbalanced logistic regression
Highdimensional data are routinely collected in many application areas....
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Stochastic Lipschitz QLearning
In an episodic Markov Decision Process (MDP) problem, an online algorith...
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Maximum Pairwise Bayes Factors for Covariance Structure Testing
Hypothesis testing of structure in covariance matrices is of significant...
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Tensor network factorizations: Relationships between brain structural connectomes and traits
Advanced brain imaging techniques make it possible to measure individual...
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NonOscillatory Pattern Learning for NonStationary Signals
This paper proposes a novel nonoscillatory pattern (NOP) learning schem...
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Symmetric Bilinear Regression for Signal Subgraph Estimation
There is increasing interest in learning a set of small outcomerelevant...
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Bayesian Mosaic: Parallelizable Composite Posterior
This paper proposes Bayesian mosaic, a parallelizable composite posterio...
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Reducing overclustering via the powered Chinese restaurant process
Dirichlet process mixture (DPM) models tend to produce many small cluste...
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Intrinsic Gaussian processes on complex constrained domains
We propose a class of intrinsic Gaussian processes (inGPs) for interpol...
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Common and Individual Structure of Multiple Networks
This article focuses on the problem of studying shared and individuals...
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DECOrrelated feature space partitioning for distributed sparse regression
Fitting statistical models is computationally challenging when the sampl...
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No penalty no tears: Least squares in highdimensional linear models
Ordinary least squares (OLS) is the default method for fitting linear mo...
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Median Selection Subset Aggregation for Parallel Inference
For massive data sets, efficient computation commonly relies on distribu...
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Bayesian crack detection in ultra high resolution multimodal images of paintings
The preservation of our cultural heritage is of paramount importance. Th...
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BetaNegative Binomial Process and Poisson Factor Analysis
A betanegative binomial (BNB) process is proposed, leading to a betaga...
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Generalized double Pareto shrinkage
We propose a generalized double Pareto prior for Bayesian shrinkage esti...
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Bayesian Nonparametric Covariance Regression
Although there is a rich literature on methods for allowing the variance...
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David Dunson
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Arts and Sciences Professor of Statistical Science, Mathematics & Electrical & Computer Engineering at Duke University, National Institute of Environmental Health Sciences at National Institutes of Health from19972008