David B. Dunson

Affiliations:
  • Duke University, Department of Statistical Science


According to our database1, David B. Dunson authored at least 144 papers between 2007 and 2024.

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Bibliography

2024
Hierarchical Shrinkage Gaussian Processes: Applications to Computer Code Emulation and Dynamical System Recovery.
SIAM/ASA J. Uncertain. Quantification, 2024

Spatial meshing for general Bayesian multivariate models.
J. Mach. Learn. Res., 2024

Fast nonparametric feature selection with error control using integrated path stability selection.
CoRR, 2024

Bayesian Joint Additive Factor Models for Multiview Learning.
CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
CoRR, 2024


2023
PPA: Principal parcellation analysis for brain connectomes and multiple traits.
NeuroImage, August, 2023

Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data.
J. Mach. Learn. Res., 2023

Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph.
J. Mach. Learn. Res., 2023

Nearest Neighbor Dirichlet Mixtures.
J. Mach. Learn. Res., 2023

Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering.
J. Mach. Learn. Res., 2023

Bayesian Transfer Learning.
CoRR, 2023

Machine Learning and the Future of Bayesian Computation.
CoRR, 2023

Ellipsoid fitting with the Cayley transform.
CoRR, 2023

Spectral Gap Regularization of Neural Networks.
CoRR, 2023

2022
Exponential-Wrapped Distributions on Symmetric Spaces.
SIAM J. Math. Data Sci., December, 2022

Gaussian Process Subspace Prediction for Model Reduction.
SIAM J. Sci. Comput., 2022

Spatial Multivariate Trees for Big Data Bayesian Regression.
J. Mach. Learn. Res., 2022

Multiscale Graph Comparison via the Embedded Laplacian Distance.
CoRR, 2022

Outlier detection for multi-network data.
Bioinform., 2022

2021
Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets.
NeuroImage, 2021

Bayesian time-aligned factor analysis of paired multivariate time series.
J. Mach. Learn. Res., 2021

Soft Tensor Regression.
J. Mach. Learn. Res., 2021

Bayesian Distance Clustering.
J. Mach. Learn. Res., 2021

Monte Carlo Simulation on the Stiefel Manifold via Polar Expansion.
J. Comput. Graph. Stat., 2021

Inferring Manifolds From Noisy Data Using Gaussian Processes.
CoRR, 2021

Gaussian Process Subspace Regression for Model Reduction.
CoRR, 2021

Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Bayesian Closed Surface Fitting Through Tensor Products.
J. Mach. Learn. Res., 2020

Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds.
CoRR, 2020

Bayesian neural networks and dimensionality reduction.
CoRR, 2020

Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity.
CoRR, 2020

Reproducible Bootstrap Aggregating.
CoRR, 2020

Projected t-SNE for batch correction.
Bioinform., 2020

Fiedler Regularization: Learning Neural Networks with Graph Sparsity.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Symmetric Bilinear Regression for Signal Subgraph Estimation.
IEEE Trans. Signal Process., 2019

Tensor network factorizations: Relationships between brain structural connectomes and traits.
NeuroImage, 2019

Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease.
Frontiers Neuroinformatics, 2019

Auto-encoding graph-valued data with applications to brain connectomes.
CoRR, 2019

Efficient Entropy Estimation for Stationary Time Series.
CoRR, 2019

Classification via local manifold approximation.
CoRR, 2019

Supervised Multiscale Dimension Reduction for Spatial Interaction Networks.
CoRR, 2019

Locally Convex Kernel Mixtures: Bayesian Subspace Learning.
Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

2018
Mapping population-based structural connectomes.
NeuroImage, 2018

Scalable Bayes via Barycenter in Wasserstein Space.
J. Mach. Learn. Res., 2018

Scaling up Data Augmentation MCMC via Calibration.
J. Mach. Learn. Res., 2018

Clustering-Enhanced Stochastic Gradient MCMC for Hidden Markov Models with Rare States.
CoRR, 2018

Non-Oscillatory Pattern Learning for Non-Stationary Signals.
CoRR, 2018

Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks.
CoRR, 2018

Reducing over-clustering via the powered Chinese restaurant process.
CoRR, 2018

Intrinsic Gaussian processes on complex constrained domains.
CoRR, 2018

Extrema-weighted feature extraction for functional data.
Bioinform., 2018

2017
Robust and Scalable Bayes via a Median of Subset Posterior Measures.
J. Mach. Learn. Res., 2017

Bayesian Tensor Regression.
J. Mach. Learn. Res., 2017

Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics.
Oper. Res., 2017

Bayesian network-response regression.
Bioinform., 2017

2016
Removing Cradle Artifacts in X-Ray Images of Paintings.
SIAM J. Imaging Sci., 2016

Subspace segmentation by dense block and sparse representation.
Neural Networks, 2016

Bayesian Graphical Models for Multivariate Functional Data.
J. Mach. Learn. Res., 2016

Compressed Gaussian Process for Manifold Regression.
J. Mach. Learn. Res., 2016

Fast moment estimation for generalized latent Dirichlet models.
CoRR, 2016

Inefficiency of Data Augmentation for Large Sample Imbalanced Data.
CoRR, 2016

Boosting Variational Inference.
CoRR, 2016

DECOrrelated feature space partitioning for distributed sparse regression.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

No penalty no tears: Least squares in high-dimensional linear models.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Scalable geometric density estimation.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Variational Gaussian Copula Inference.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Bayesian nonparametric covariance regression.
J. Mach. Learn. Res., 2015

A hybrid bayesian approach for genome-wide association studies on related individuals.
Bioinform., 2015

Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach.
Proceedings of the 9th ACM Conference on Recommender Systems, 2015

On the consistency theory of high dimensional variable screening.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Parallelizing MCMC with Random Partition Trees.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Quantifying uncertainty in variable selection with arbitrary matrices.
Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015

WASP: Scalable Bayes via barycenters of subset posteriors.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling.
IEEE Trans. Biomed. Eng., 2014

Semiconvex Regression for Metamodeling-Based Optimization.
SIAM J. Optim., 2014

Adaptive sampling for Bayesian geospatial models.
Stat. Comput., 2014

Improving prediction from dirichlet process mixtures via enrichment.
J. Mach. Learn. Res., 2014

Locally adaptive factor processes for multivariate time series.
J. Mach. Learn. Res., 2014

Special issue on Bayesian computing, methods and applications.
Comput. Stat. Data Anal., 2014

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14).
CoRR, 2014

Bayesian Conditional Density Filtering for Big Data.
CoRR, 2014

Learning phenotype densities conditional on many interacting predictors.
Bioinform., 2014

Median Selection Subset Aggregation for Parallel Inference.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors.
Proceedings of the 31th International Conference on Machine Learning, 2014

Scalable and Robust Bayesian Inference via the Median Posterior.
Proceedings of the 31th International Conference on Machine Learning, 2014

Digital cradle removal in X-ray images of art paintings.
Proceedings of the 2014 IEEE International Conference on Image Processing, 2014

Bayesian Logistic Gaussian Process Models for Dynamic Networks.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Deep Learning with Hierarchical Convolutional Factor Analysis.
IEEE Trans. Pattern Anal. Mach. Intell., 2013

Posterior consistency in conditional distribution estimation.
J. Multivar. Anal., 2013

Multivariate convex regression with adaptive partitioning.
J. Mach. Learn. Res., 2013

Analysis of space-time relational data with application to legislative voting.
Comput. Stat. Data Anal., 2013

Learning Densities Conditional on Many Interacting Features
CoRR, 2013

Bayesian Compressed Regression
CoRR, 2013

Parallel MCMC via Weierstrass Sampler.
CoRR, 2013

Bayesian consensus clustering.
Bioinform., 2013

Multiscale Dictionary Learning for Estimating Conditional Distributions.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Locally Adaptive Bayesian Multivariate Time Series.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Bayesian crack detection in ultra high resolution multimodal images of paintings.
Proceedings of the 18th International Conference on Digital Signal Processing, 2013

Diagonal Orthant Multinomial Probit Models.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

Bayesian learning of joint distributions of objects.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images.
IEEE Trans. Image Process., 2012

High Dimensional Longitudinal Genomic Data: An analysis used for monitoring viral infections.
IEEE Signal Process. Mag., 2012

Semiparametric Bayesian local functional models for diffusion tensor tract statistics.
NeuroImage, 2012

Nonparametric Bayes classification and hypothesis testing on manifolds.
J. Multivar. Anal., 2012

Beta-Negative Binomial Process and Poisson Factor Analysis.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Hierarchical Latent Dictionaries for Models of Brain Activation.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Repulsive Mixtures.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Multiresolution Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Lognormal and Gamma Mixed Negative Binomial Regression.
Proceedings of the 29th International Conference on Machine Learning, 2012

Bayesian Watermark Attacks.
Proceedings of the 29th International Conference on Machine Learning, 2012

Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Corrections to "Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds".
IEEE Trans. Signal Process., 2011

Bayesian Local Contamination Models for Multivariate Outliers.
Technometrics, 2011

Learning Low-Dimensional Signal Models.
IEEE Signal Process. Mag., 2011

Bayesian Inference for Genomic Data Integration Reduces Misclassification Rate in Predicting Protein-Protein Interactions.
PLoS Comput. Biol., 2011

Dependent Hierarchical Beta Process for Image Interpolation and Denoising.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Logistic Stick-Breaking Process.
J. Mach. Learn. Res., 2011

Preface.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

The Kernel Beta Process.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Generalized Beta Mixtures of Gaussians.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Tree-Structured Infinite Sparse Factor Model.
Proceedings of the 28th International Conference on Machine Learning, 2011

Approximate Dynamic Programming for Storage Problems.
Proceedings of the 28th International Conference on Machine Learning, 2011

The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning.
Proceedings of the 28th International Conference on Machine Learning, 2011

Topic Modeling with Nonparametric Markov Tree.
Proceedings of the 28th International Conference on Machine Learning, 2011

Covariate-dependent dictionary learning and sparse coding.
Proceedings of the IEEE International Conference on Acoustics, 2011

2010
Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds.
IEEE Trans. Signal Process., 2010

Probabilistic Topic Models.
IEEE Signal Process. Mag., 2010

Classification with Incomplete Data Using Dirichlet Process Priors.
J. Mach. Learn. Res., 2010

Semiparametric Bayes hierarchical models with mean and variance constraints.
Comput. Stat. Data Anal., 2010

Bayesian Inference of the Number of Factors in Gene-Expression Analysis: Application to Human Virus Challenge Studies.
BMC Bioinform., 2010

Joint Analysis of Time-Evolving Binary Matrices and Associated Documents.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009
Multitask Compressive Sensing.
IEEE Trans. Signal Process., 2009

A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Multi-task classification with infinite local experts.
Proceedings of the IEEE International Conference on Acoustics, 2009

Music analysis with a Bayesian dynamic model.
Proceedings of the IEEE International Conference on Acoustics, 2009

2008
Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data.
IEEE Trans. Signal Process., 2008

The dynamic hierarchical Dirichlet process.
Proceedings of the Machine Learning, 2008

Multi-task compressive sensing with Dirichlet process priors.
Proceedings of the Machine Learning, 2008

Hierarchical kernel stick-breaking process for multi-task image analysis.
Proceedings of the Machine Learning, 2008

2007
The matrix stick-breaking process for flexible multi-task learning.
Proceedings of the Machine Learning, 2007

Multi-task learning for sequential data via iHMMs and the nested Dirichlet process.
Proceedings of the Machine Learning, 2007


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