David B. Dunson

According to our database, David B. Dunson authored at least 138 papers between 2007 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

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

Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

CoRR, 2024

2023

PPA: Principal parcellation analysis for brain connectomes and multiple traits.

Rongjie Liu, Meng Li, David B. Dunson

NeuroImage, August, 2023

Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data.

Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson

J. Mach. Learn. Res., 2023

Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering.

Noirrit Kiran Chandra, Antonio Canale, David B. Dunson

J. Mach. Learn. Res., 2023

Bayesian Transfer Learning.

Piotr M. Suder, Jason Xu, David B. Dunson

CoRR, 2023

Machine Learning and the Future of Bayesian Computation.

Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson

CoRR, 2023

Ellipsoid fitting with the Cayley transform.

Omar Melikechi, David B. Dunson

CoRR, 2023

Spectral Gap Regularization of Neural Networks.

Edric Tam, David B. Dunson

CoRR, 2023

Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery.

Tao Tang, Simon Mak, David B. Dunson

CoRR, 2023

2022

Exponential-Wrapped Distributions on Symmetric Spaces.

Emmanuel Chevallier, Didong Li, Yulong Lu, David B. Dunson

SIAM J. Math. Data Sci., December, 2022

Gaussian Process Subspace Prediction for Model Reduction.

Ruda Zhang, Simon Mak, David B. Dunson

SIAM J. Sci. Comput., 2022

Spatial Multivariate Trees for Big Data Bayesian Regression.

Michele Peruzzi, David B. Dunson

J. Mach. Learn. Res., 2022

Multiscale Graph Comparison via the Embedded Laplacian Distance.

Edric Tam, David B. Dunson

CoRR, 2022

Outlier detection for multi-network data.

Pritam Dey, Zhengwu Zhang, David B. Dunson

Bioinform., 2022

2021

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

Meimei Liu, Zhengwu Zhang, David B. Dunson

NeuroImage, 2021

Bayesian time-aligned factor analysis of paired multivariate time series.

Arkaprava Roy, Jana Schaich Borg, David B. Dunson

J. Mach. Learn. Res., 2021

Soft Tensor Regression.

Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson

J. Mach. Learn. Res., 2021

Bayesian Distance Clustering.

Leo L. Duan, David B. Dunson

J. Mach. Learn. Res., 2021

Monte Carlo Simulation on the Stiefel Manifold via Polar Expansion.

Michael Jauch, Peter D. Hoff, David B. Dunson

J. Comput. Graph. Stat., 2021

Inferring Manifolds From Noisy Data Using Gaussian Processes.

David B. Dunson, Nan Wu

CoRR, 2021

Gaussian Process Subspace Regression for Model Reduction.

Ruda Zhang, Simon Mak, David B. Dunson

CoRR, 2021

Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference.

Sean Plummer, Shuang Zhou, Anirban Bhattacharya, David B. Dunson, Debdeep Pati

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Bayesian Closed Surface Fitting Through Tensor Products.

Olivier Binette, Debdeep Pati, David B. Dunson

J. Mach. Learn. Res., 2020

Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds.

Lizhen Lin, Bayan Saparbayeva, Michael Minyi Zhang, David B. Dunson

CoRR, 2020

Bayesian neural networks and dimensionality reduction.

Deborshee Sen, Theodore Papamarkou, David B. Dunson

CoRR, 2020

Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity.

Austin Talbot, David B. Dunson, Kafui Dzirasa, David E. Carlson

CoRR, 2020

Reproducible Bootstrap Aggregating.

Meimei Liu, David B. Dunson

CoRR, 2020

Projected t-SNE for batch correction.

Emanuele Aliverti, Jeffrey L. Tilson, Dayne L. Filer, Benjamin Babcock, Alejandro Colaneri, Jennifer Ocasio, Timothy R. Gershon, Kirk C. Wilhelmsen, David B. Dunson

Bioinform., 2020

Fiedler Regularization: Learning Neural Networks with Graph Sparsity.

Edric Tam, David B. Dunson

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Symmetric Bilinear Regression for Signal Subgraph Estimation.

Lu Wang, Zhengwu Zhang, David B. Dunson

IEEE Trans. Signal Process., 2019

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

Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu, David B. Dunson

NeuroImage, 2019

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

Alexandra Badea, Wenlin Wu, Jordan Shuff, Michele Wang, Robert J. Anderson, Yi Qi, G. Allan Johnson, Joan G. Wilson, Serge Koudoro, Eleftherios Garyfallidis, Carol A. Colton, David B. Dunson

Frontiers Neuroinformatics, 2019

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

Meimei Liu, Zhengwu Zhang, David B. Dunson

CoRR, 2019

Efficient Entropy Estimation for Stationary Time Series.

Alexander L. Young, David B. Dunson

CoRR, 2019

Classification via local manifold approximation.

Didong Li, David B. Dunson

CoRR, 2019

Supervised Multiscale Dimension Reduction for Spatial Interaction Networks.

Shaobo Han, David B. Dunson

CoRR, 2019

Locally Convex Kernel Mixtures: Bayesian Subspace Learning.

Duy Hoang Thai, Hau-Tieng Wu, David B. Dunson

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

2018

Mapping population-based structural connectomes.

Zhengwu Zhang, Maxime Descoteaux, Jingwen Zhang, Gabriel Girard, Maxime Chamberland, David B. Dunson, Anuj Srivastava, Hongtu Zhu

NeuroImage, 2018

Scalable Bayes via Barycenter in Wasserstein Space.

Sanvesh Srivastava, Cheng Li, David B. Dunson

J. Mach. Learn. Res., 2018

Scaling up Data Augmentation MCMC via Calibration.

Leo L. Duan, James E. Johndrow, David B. Dunson

J. Mach. Learn. Res., 2018

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

Rihui Ou, Alexander L. Young, David B. Dunson

CoRR, 2018

Non-Oscillatory Pattern Learning for Non-Stationary Signals.

Jieren Xu, Yitong Li, David B. Dunson, Ingrid Daubechies, Haizhao Yang

CoRR, 2018

Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks.

Shaobo Han, David B. Dunson

CoRR, 2018

Reducing over-clustering via the powered Chinese restaurant process.

Jun Lu, Meng Li, David B. Dunson

CoRR, 2018

Intrinsic Gaussian processes on complex constrained domains.

Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David B. Dunson

CoRR, 2018

Extrema-weighted feature extraction for functional data.

Willem van den Boom, Callie Mao, Rebecca A. Schroeder, David B. Dunson

Bioinform., 2018

2017

Robust and Scalable Bayes via a Median of Subset Posterior Measures.

Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson

J. Mach. Learn. Res., 2017

Bayesian Tensor Regression.

Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson

J. Mach. Learn. Res., 2017

Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics.

Yan Shang, David B. Dunson, Jing-Sheng Song

Oper. Res., 2017

Bayesian network-response regression.

Lu Wang, Daniele Durante, Rex E. Jung, David B. Dunson

Bioinform., 2017

2016

Removing Cradle Artifacts in X-Ray Images of Paintings.

Rujie Yin, Bruno Cornelis, Gábor Fodor, Noelle Ocon, David B. Dunson, Ingrid Daubechies

SIAM J. Imaging Sci., 2016

Subspace segmentation by dense block and sparse representation.

Kewei Tang, David B. Dunson, Zhixun Su, Risheng Liu, Jie Zhang, Jiangxin Dong

Neural Networks, 2016

Bayesian Graphical Models for Multivariate Functional Data.

Hongxiao Zhu, Nate Strawn, David B. Dunson

J. Mach. Learn. Res., 2016

Compressed Gaussian Process for Manifold Regression.

Rajarshi Guhaniyogi, David B. Dunson

J. Mach. Learn. Res., 2016

Fast moment estimation for generalized latent Dirichlet models.

Shiwen Zhao, Barbara E. Engelhardt, Sayan Mukherjee, David B. Dunson

CoRR, 2016

Inefficiency of Data Augmentation for Large Sample Imbalanced Data.

James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson

CoRR, 2016

Boosting Variational Inference.

Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson

CoRR, 2016

DECOrrelated feature space partitioning for distributed sparse regression.

Xiangyu Wang, David B. Dunson, Chenlei Leng

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.

Xiangyu Wang, David B. Dunson, Chenlei Leng

Proceedings of the 33nd International Conference on Machine Learning, 2016

Scalable geometric density estimation.

Ye Wang, Antonio Canale, David B. Dunson

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Variational Gaussian Copula Inference.

Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Bayesian nonparametric covariance regression.

Emily B. Fox, David B. Dunson

J. Mach. Learn. Res., 2015

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

A. Yazdani, David B. Dunson

Bioinform., 2015

Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach.

Fangjian Guo, David B. Dunson

Proceedings of the 9th ACM Conference on Recommender Systems, 2015

On the consistency theory of high dimensional variable screening.

Xiangyu Wang, Chenlei Leng, David B. Dunson

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.

Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson

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.

Ye Wang, David B. Dunson

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.

Willem van den Boom, David B. Dunson, Galen Reeves

Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015

WASP: Scalable Bayes via barycenters of subset posteriors.

Sanvesh Srivastava, Volkan Cevher, Quoc Tran-Dinh, David B. Dunson

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling.

David E. Carlson, Joshua T. Vogelstein, Qisong Wu, Wenzhao Lian, Mingyuan Zhou, Colin R. Stoetzner, Daryl Kipke, Douglas Weber, David B. Dunson, Lawrence Carin

IEEE Trans. Biomed. Eng., 2014

Semiconvex Regression for Metamodeling-Based Optimization.

Lauren A. Hannah, Warren B. Powell, David B. Dunson

SIAM J. Optim., 2014

Adaptive sampling for Bayesian geospatial models.

Hongxia Yang, Fei Liu, Chunlin Ji, David B. Dunson

Stat. Comput., 2014

Improving prediction from dirichlet process mixtures via enrichment.

Sara Wade, David B. Dunson, Sonia Petrone, Lorenzo Trippa

J. Mach. Learn. Res., 2014

Locally adaptive factor processes for multivariate time series.

Daniele Durante, Bruno Scarpa, David B. Dunson

J. Mach. Learn. Res., 2014

Special issue on Bayesian computing, methods and applications.

Cathy W. S. Chen, David B. Dunson, Sylvia Frühwirth-Schnatter, Stephen G. Walker

Comput. Stat. Data Anal., 2014

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

Laurent Jacques, Christophe De Vleeschouwer, Yannick Boursier, Prasad Sudhakar, C. De Mol, Aleksandra Pizurica, Sandrine Anthoine, Pierre Vandergheynst, Pascal Frossard, Cagdas Bilen, Srdan Kitic, Nancy Bertin, Rémi Gribonval, Nicolas Boumal, Bamdev Mishra, Pierre-Antoine Absil, Rodolphe Sepulchre, Shaun Bundervoet, Colas Schretter, Ann Dooms, Peter Schelkens, Olivier Chabiron, François Malgouyres, Jean-Yves Tourneret, Nicolas Dobigeon, Pierre Chainais, Cédric Richard, Bruno Cornelis, Ingrid Daubechies, David B. Dunson, Marie Danková, Pavel Rajmic, Kévin Degraux, Valerio Cambareri, Bert Geelen, Gauthier Lafruit, Gianluca Setti, Jean-François Determe, Jérôme Louveaux, François Horlin, Angélique Drémeau, Patrick Héas, Cédric Herzet, Vincent Duval, Gabriel Peyré, Alhussein Fawzi, Mike E. Davies, Nicolas Gillis, Stephen A. Vavasis, Charles Soussen, Luc Le Magoarou, Jingwei Liang, Jalal Fadili, Antoine Liutkus, David Martina, Sylvain Gigan, Laurent Daudet, Mauro Maggioni, Stanislav Minsker, Nate Strawn, C. Mory, Fred Maurice Ngolè Mboula, Jean-Luc Starck, Ignace Loris, Samuel Vaiter, Mohammad Golbabaee, Dejan Vukobratovic

CoRR, 2014

Bayesian Conditional Density Filtering for Big Data.

Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson

CoRR, 2014

Learning phenotype densities conditional on many interacting predictors.

David C. Kessler, Jack A. Taylor, David B. Dunson

Bioinform., 2014

Median Selection Subset Aggregation for Parallel Inference.

Xiangyu Wang, Peichao Peng, David B. Dunson

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.

Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David B. Dunson, Lawrence Carin

Proceedings of the 31th International Conference on Machine Learning, 2014

Scalable and Robust Bayesian Inference via the Median Posterior.

Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson

Proceedings of the 31th International Conference on Machine Learning, 2014

Digital cradle removal in X-ray images of art paintings.

Rujie Yin, David B. Dunson, Bruno Cornelis, Bill Brown, Noelle Ocon, Ingrid Daubechies

Proceedings of the 2014 IEEE International Conference on Image Processing, 2014

Bayesian Logistic Gaussian Process Models for Dynamic Networks.

Daniele Durante, David B. Dunson

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Deep Learning with Hierarchical Convolutional Factor Analysis.

Bo Chen, Gungor Polatkan, Guillermo Sapiro, David M. Blei, David B. Dunson, Lawrence Carin

IEEE Trans. Pattern Anal. Mach. Intell., 2013

Posterior consistency in conditional distribution estimation.

Debdeep Pati, David B. Dunson, Surya T. Tokdar

J. Multivar. Anal., 2013

Multivariate convex regression with adaptive partitioning.

Lauren Hannah, David B. Dunson

J. Mach. Learn. Res., 2013

Analysis of space-time relational data with application to legislative voting.

Esther Salazar, David B. Dunson, Lawrence Carin

Comput. Stat. Data Anal., 2013

Learning Densities Conditional on Many Interacting Features

David C. Kessler, Jack A. Taylor, David B. Dunson

CoRR, 2013

Bayesian Compressed Regression

Rajarshi Guhaniyogi, David B. Dunson

CoRR, 2013

Parallel MCMC via Weierstrass Sampler.

Xiangyu Wang, David B. Dunson

CoRR, 2013

Bayesian consensus clustering.

Eric F. Lock, David B. Dunson

Bioinform., 2013

Multiscale Dictionary Learning for Estimating Conditional Distributions.

Francesca Petralia, Joshua T. Vogelstein, David B. Dunson

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.

Daniele Durante, Bruno Scarpa, David B. Dunson

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.

Bruno Cornelis, Yun Yang, Joshua T. Vogelstein, Ann Dooms, Ingrid Daubechies, David B. Dunson

Proceedings of the 18th International Conference on Digital Signal Processing, 2013

Diagonal Orthant Multinomial Probit Models.

James E. Johndrow, David B. Dunson, Kristian Lum

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

Bayesian learning of joint distributions of objects.

Anjishnu Banerjee, Jared Murray, David B. Dunson

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images.

Mingyuan Zhou, Haojun Chen, John W. Paisley, Lu Ren, Lingbo Li, Zhengming Xing, David B. Dunson, Guillermo Sapiro, Lawrence Carin

IEEE Trans. Image Process., 2012

High Dimensional Longitudinal Genomic Data: An analysis used for monitoring viral infections.

Lawrence Carin, Alfred O. Hero III, Joseph E. Lucas, David B. Dunson, Minhua Chen, Ricardo Henao, Arnau Tibau Puig, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg

IEEE Signal Process. Mag., 2012

Semiparametric Bayesian local functional models for diffusion tensor tract statistics.

Zhaowei Hua, David B. Dunson, John H. Gilmore, Martin Andreas Styner, Hongtu Zhu

NeuroImage, 2012

Nonparametric Bayes classification and hypothesis testing on manifolds.

Abhishek Bhattacharya, David B. Dunson

J. Multivar. Anal., 2012

Beta-Negative Binomial Process and Poisson Factor Analysis.

Mingyuan Zhou, Lauren Hannah, David B. Dunson, Lawrence Carin

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Hierarchical Latent Dictionaries for Models of Brain Activation.

Alona Fyshe, Emily B. Fox, David B. Dunson, Tom M. Mitchell

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Repulsive Mixtures.

Francesca Petralia, Vinayak A. Rao, David B. Dunson

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.

Emily B. Fox, David B. Dunson

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.

Mingyuan Zhou, Lingbo Li, David B. Dunson, Lawrence Carin

Proceedings of the 29th International Conference on Machine Learning, 2012

Bayesian Watermark Attacks.

Ivo Shterev, David B. Dunson

Proceedings of the 29th International Conference on Machine Learning, 2012

Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design.

Lauren Hannah, David B. Dunson

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".

Minhua Chen, Jorge G. Silva, John W. Paisley, Chunping Wang, David B. Dunson, Lawrence Carin

IEEE Trans. Signal Process., 2011

Bayesian Local Contamination Models for Multivariate Outliers.

Garritt L. Page, David B. Dunson

Technometrics, 2011

Learning Low-Dimensional Signal Models.

Lawrence Carin, Richard G. Baraniuk, Volkan Cevher, David B. Dunson, Michael I. Jordan, Guillermo Sapiro, Michael B. Wakin

IEEE Signal Process. Mag., 2011

Bayesian Inference for Genomic Data Integration Reduces Misclassification Rate in Predicting Protein-Protein Interactions.

Chuanhua Xing, David B. Dunson

PLoS Comput. Biol., 2011

Dependent Hierarchical Beta Process for Image Interpolation and Denoising.

Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David B. Dunson, Lawrence Carin

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

Logistic Stick-Breaking Process.

Lu Ren, Lan Du, Lawrence Carin, David B. Dunson

J. Mach. Learn. Res., 2011

Preface.

Geoffrey J. Gordon, David B. Dunson

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

Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices.

XianXing Zhang, David B. Dunson, Lawrence Carin

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.

Lu Ren, Yingjian Wang, David B. Dunson, Lawrence Carin

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.

Artin Armagan, David B. Dunson, Merlise Clyde

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.

XianXing Zhang, David B. Dunson, Lawrence Carin

Proceedings of the 28th International Conference on Machine Learning, 2011

Approximate Dynamic Programming for Storage Problems.

Lauren Hannah, David B. Dunson

Proceedings of the 28th International Conference on Machine Learning, 2011

The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning.

Bo Chen, Gungor Polatkan, Guillermo Sapiro, David B. Dunson, Lawrence Carin

Proceedings of the 28th International Conference on Machine Learning, 2011

Topic Modeling with Nonparametric Markov Tree.

Haojun Chen, David B. Dunson, Lawrence Carin

Proceedings of the 28th International Conference on Machine Learning, 2011

Covariate-dependent dictionary learning and sparse coding.

Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David B. Dunson, Lawrence Carin

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.

Minhua Chen, Jorge G. Silva, John W. Paisley, Chunping Wang, David B. Dunson, Lawrence Carin

IEEE Trans. Signal Process., 2010

Probabilistic Topic Models.

David M. Blei, Lawrence Carin, David B. Dunson

IEEE Signal Process. Mag., 2010

Classification with Incomplete Data Using Dirichlet Process Priors.

Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson

J. Mach. Learn. Res., 2010

Semiparametric Bayes hierarchical models with mean and variance constraints.

Mingan Yang, David B. Dunson, Donna D. Baird

Comput. Stat. Data Anal., 2010

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

Bo Chen, Minhua Chen, John W. Paisley, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Alfred O. Hero III, Joseph E. Lucas, David B. Dunson, Lawrence Carin

BMC Bioinform., 2010

Joint Analysis of Time-Evolving Binary Matrices and Associated Documents.

Eric Wang, Dehong Liu, Jorge G. Silva, David B. Dunson, Lawrence Carin

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.

Shihao Ji, David B. Dunson, Lawrence Carin

IEEE Trans. Signal Process., 2009

A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation.

Lan Du, Lu Ren, David B. Dunson, Lawrence Carin

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.

Chunping Wang, Qi An, Lawrence Carin, David B. Dunson

Proceedings of the IEEE International Conference on Acoustics, 2009

Music analysis with a Bayesian dynamic model.

Lu Ren, David B. Dunson, Scott Lindroth, Lawrence Carin

Proceedings of the IEEE International Conference on Acoustics, 2009

2008

Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data.

Kai Ni, John W. Paisley, Lawrence Carin, David B. Dunson

IEEE Trans. Signal Process., 2008

The dynamic hierarchical Dirichlet process.

Lu Ren, David B. Dunson, Lawrence Carin

Proceedings of the Machine Learning, 2008

Multi-task compressive sensing with Dirichlet process priors.

Yuting Qi, Dehong Liu, David B. Dunson, Lawrence Carin

Proceedings of the Machine Learning, 2008

Hierarchical kernel stick-breaking process for multi-task image analysis.

Qi An, Chunping Wang, Ivo Shterev, Eric Wang, Lawrence Carin, David B. Dunson

Proceedings of the Machine Learning, 2008

2007

The matrix stick-breaking process for flexible multi-task learning.

Ya Xue, David B. Dunson, Lawrence Carin

Proceedings of the Machine Learning, 2007

Multi-task learning for sequential data via iHMMs and the nested Dirichlet process.

Kai Ni, Lawrence Carin, David B. Dunson

Proceedings of the Machine Learning, 2007