Multi-task learning for sequential data via iHMMs and the nested Dirichlet process. - Kai Ni 007; Lawrence Carin; David B. Dunson (ICML, 2007)

The matrix stick-breaking process for flexible multi-task learning. - Ya Xue; David B. Dunson; Lawrence Carin (ICML, 2007)

Hierarchical kernel stick-breaking process for multi-task image analysis. - Qi An; Chunping Wang 001; Ivo Shterev; Eric Wang; Lawrence Carin; David B. Dunson (ICML, 2008)

Multi-task compressive sensing with Dirichlet process priors. - Yuting Qi; Dehong Liu; David B. Dunson; Lawrence Carin (ICML, 2008)

The dynamic hierarchical Dirichlet process. - Lu Ren; David B. Dunson; Lawrence Carin (ICML, 2008)

Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data. - Kai Ni 007; John W. Paisley; Lawrence Carin; David B. Dunson (IEEE Trans. Signal Process., 2008)

Music analysis with a Bayesian dynamic model. - Lu Ren; David B. Dunson; Scott Lindroth; Lawrence Carin (ICASSP, 2009)

Multi-task classification with infinite local experts. - Chunping Wang 001; Qi An; Lawrence Carin; David B. Dunson (ICASSP, 2009)

A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation. - Lan Du 001; Lu Ren; David B. Dunson; Lawrence Carin (NIPS, 2009)

Multitask Compressive Sensing. - Shihao Ji 001; David B. Dunson; Lawrence Carin (IEEE Trans. Signal Process., 2009)

Joint Analysis of Time-Evolving Binary Matrices and Associated Documents. - Eric Wang; Dehong Liu; Jorge G. Silva; David B. Dunson; Lawrence Carin (NIPS, 2010)

Bayesian Inference of the Number of Factors in Gene-Expression Analysis: Application to Human Virus Challenge Studies. - Bo Chen 001; 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)

Semiparametric Bayes hierarchical models with mean and variance constraints. - Mingan Yang; David B. Dunson; Donna D. Baird (Comput. Stat. Data Anal., 2010)

Classification with Incomplete Data Using Dirichlet Process Priors. - Chunping Wang 001; Xuejun Liao; Lawrence Carin; David B. Dunson (J. Mach. Learn. Res., 2010)

Probabilistic Topic Models. - David M. Blei; Lawrence Carin; David B. Dunson (IEEE Signal Process. Mag., 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 001; David B. Dunson; Lawrence Carin (IEEE Trans. Signal Process., 2010)

Covariate-dependent dictionary learning and sparse coding. - Mingyuan Zhou; Hongxia Yang; Guillermo Sapiro; David B. Dunson; Lawrence Carin (ICASSP, 2011)

Topic Modeling with Nonparametric Markov Tree. - Haojun Chen; David B. Dunson; Lawrence Carin (ICML, 2011)

The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning. - Bo Chen 001; Gungor Polatkan; Guillermo Sapiro; David B. Dunson; Lawrence Carin (ICML, 2011)

Approximate Dynamic Programming for Storage Problems. - Lauren Hannah; David B. Dunson (ICML, 2011)

Tree-Structured Infinite Sparse Factor Model. - XianXing Zhang; David B. Dunson; Lawrence Carin (ICML, 2011)

Generalized Beta Mixtures of Gaussians. - Artin Armagan; David B. Dunson; Merlise A. Clyde (NIPS, 2011)

The Kernel Beta Process. - Lu Ren; Yingjian Wang 004; David B. Dunson; Lawrence Carin (NIPS, 2011)

Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices. - XianXing Zhang; David B. Dunson; Lawrence Carin (NIPS, 2011)

Preface. - Geoffrey J. Gordon; David B. Dunson (AISTATS, 2011)

Logistic Stick-Breaking Process. - Lu Ren; Lan Du 001; Lawrence Carin; David B. Dunson (J. Mach. Learn. Res., 2011)

Dependent Hierarchical Beta Process for Image Interpolation and Denoising. - Mingyuan Zhou; Hongxia Yang; Guillermo Sapiro; David B. Dunson; Lawrence Carin (AISTATS, 2011)

Bayesian Inference for Genomic Data Integration Reduces Misclassification Rate in Predicting Protein-Protein Interactions. - Chuanhua Xing; David B. Dunson (PLoS Comput. Biol., 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 Local Contamination Models for Multivariate Outliers. - Garritt L. Page; David B. Dunson (Technometrics, 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 001; David B. Dunson; Lawrence Carin (IEEE Trans. Signal Process., 2011)

Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design. - Lauren Hannah; David B. Dunson (ICML, 2012)

Bayesian Watermark Attacks. - Ivo Shterev; David B. Dunson (ICML, 2012)

Lognormal and Gamma Mixed Negative Binomial Regression. - Mingyuan Zhou; Lingbo Li 002; David B. Dunson; Lawrence Carin (ICML, 2012)

Multiresolution Gaussian Processes. - Emily B. Fox; David B. Dunson (NIPS, 2012)

Repulsive Mixtures. - Francesca Petralia; Vinayak A. Rao; David B. Dunson (NIPS, 2012)

Hierarchical Latent Dictionaries for Models of Brain Activation. - Alona Fyshe; Emily B. Fox; David B. Dunson; Tom M. Mitchell (AISTATS, 2012)

Beta-Negative Binomial Process and Poisson Factor Analysis. - Mingyuan Zhou; Lauren Hannah; David B. Dunson; Lawrence Carin (AISTATS, 2012)

Nonparametric Bayes classification and hypothesis testing on manifolds. - Abhishek Bhattacharya; David B. Dunson (J. Multivar. Anal., 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)

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)

Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images. - Mingyuan Zhou; Haojun Chen; John W. Paisley; Lu Ren; Lingbo Li 002; Zhengming Xing; David B. Dunson; Guillermo Sapiro; Lawrence Carin (IEEE Trans. Image Process., 2012)

Bayesian learning of joint distributions of objects. - Anjishnu Banerjee; Jared Murray; David B. Dunson (AISTATS, 2013)

Diagonal Orthant Multinomial Probit Models. - James E. Johndrow; David B. Dunson; Kristian Lum (AISTATS, 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 (DSP, 2013)

Locally Adaptive Bayesian Multivariate Time Series. - Daniele Durante; Bruno Scarpa; David B. Dunson (NIPS, 2013)

Multiscale Dictionary Learning for Estimating Conditional Distributions. - Francesca Petralia; Joshua T. Vogelstein; David B. Dunson (NIPS, 2013)

Bayesian consensus clustering. - Eric F. Lock; David B. Dunson (Bioinform., 2013)

Parallel MCMC via Weierstrass Sampler. - Xiangyu Wang 006; David B. Dunson (CoRR, 2013)

Bayesian Compressed Regression - Rajarshi Guhaniyogi; David B. Dunson (CoRR, 2013)

Learning Densities Conditional on Many Interacting Features - David C. Kessler; Jack A. Taylor; David B. Dunson (CoRR, 2013)

Analysis of space-time relational data with application to legislative voting. - Esther Salazar; David B. Dunson; Lawrence Carin (Comput. Stat. Data Anal., 2013)

Multivariate convex regression with adaptive partitioning. - Lauren Hannah; David B. Dunson (J. Mach. Learn. Res., 2013)

Posterior consistency in conditional distribution estimation. - Debdeep Pati; David B. Dunson; Surya T. Tokdar (J. Multivar. Anal., 2013)

Deep Learning with Hierarchical Convolutional Factor Analysis. - Bo Chen 001; Gungor Polatkan; Guillermo Sapiro; David M. Blei; David B. Dunson; Lawrence Carin (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Bayesian Logistic Gaussian Process Models for Dynamic Networks. - Daniele Durante; David B. Dunson (AISTATS, 2014)

Digital cradle removal in X-ray images of art paintings. - Rujie Yin; David B. Dunson; Bruno Cornelis; Bill Brown; Noelle Ocon; Ingrid Daubechies (ICIP, 2014)

Scalable and Robust Bayesian Inference via the Median Posterior. - Stanislav Minsker; Sanvesh Srivastava; Lizhen Lin; David B. Dunson (ICML, 2014)

Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors. - Piyush Rai; Yingjian Wang 004; Shengbo Guo; Gary Chen; David B. Dunson; Lawrence Carin (ICML, 2014)

Median Selection Subset Aggregation for Parallel Inference. - Xiangyu Wang 006; Peichao Peng; David B. Dunson (NIPS, 2014)

Learning phenotype densities conditional on many interacting predictors. - David C. Kessler; Jack A. Taylor; David B. Dunson (Bioinform., 2014)

Bayesian Conditional Density Filtering for Big Data. - Rajarshi Guhaniyogi; Shaan Qamar; David B. Dunson (CoRR, 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; et al. (CoRR, 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)

Locally adaptive factor processes for multivariate time series. - Daniele Durante; Bruno Scarpa; David B. Dunson (J. Mach. Learn. Res., 2014)

Improving prediction from dirichlet process mixtures via enrichment. - Sara Wade; David B. Dunson; Sonia Petrone; Lorenzo Trippa (J. Mach. Learn. Res., 2014)

Adaptive sampling for Bayesian geospatial models. - Hongxia Yang; Fei Liu 025; Chunlin Ji; David B. Dunson (Stat. Comput., 2014)

Semiconvex Regression for Metamodeling-Based Optimization. - Lauren A. Hannah; Warren B. Powell; David B. Dunson (SIAM J. Optim., 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)

WASP: Scalable Bayes via barycenters of subset posteriors. - Sanvesh Srivastava; Volkan Cevher; Quoc Tran-Dinh; David B. Dunson (AISTATS, 2015)

Quantifying uncertainty in variable selection with arbitrary matrices. - Willem van den Boom; David B. Dunson; Galen Reeves (CAMSAP, 2015)

Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process. - Ye Wang; David B. Dunson (NIPS, 2015)

Parallelizing MCMC with Random Partition Trees. - Xiangyu Wang 006; Fangjian Guo; Katherine A. Heller; David B. Dunson (NIPS, 2015)

On the consistency theory of high dimensional variable screening. - Xiangyu Wang 006; Chenlei Leng; David B. Dunson (NIPS, 2015)

Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach. - Fangjian Guo; David B. Dunson (RecSys, 2015)

A hybrid bayesian approach for genome-wide association studies on related individuals. - Akram Yazdani; David B. Dunson (Bioinform., 2015)

Bayesian nonparametric covariance regression. - Emily B. Fox; David B. Dunson (J. Mach. Learn. Res., 2015)

Variational Gaussian Copula Inference. - Shaobo Han; Xuejun Liao; David B. Dunson; Lawrence Carin (AISTATS, 2016)

Scalable geometric density estimation. - Ye Wang; Antonio Canale; David B. Dunson (AISTATS, 2016)

No penalty no tears: Least squares in high-dimensional linear models. - Xiangyu Wang 006; David B. Dunson; Chenlei Leng (ICML, 2016)

DECOrrelated feature space partitioning for distributed sparse regression. - Xiangyu Wang 006; David B. Dunson; Chenlei Leng (NIPS, 2016)

Boosting Variational Inference. - Fangjian Guo; Xiangyu Wang 006; Kai Fan 002; Tamara Broderick; 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)

Fast moment estimation for generalized latent Dirichlet models. - Shiwen Zhao; Barbara E. Engelhardt; Sayan Mukherjee 001; David B. Dunson (CoRR, 2016)

Compressed Gaussian Process for Manifold Regression. - Rajarshi Guhaniyogi; David B. Dunson (J. Mach. Learn. Res., 2016)

Bayesian Graphical Models for Multivariate Functional Data. - Hongxiao Zhu; Nate Strawn; David B. Dunson (J. Mach. Learn. Res., 2016)

Subspace segmentation by dense block and sparse representation. - Kewei Tang; David B. Dunson; Zhixun Su; Risheng Liu; Jie Zhang 056; Jiangxin Dong (Neural Networks, 2016)

Removing Cradle Artifacts in X-Ray Images of Paintings. - Rujie Yin; Bruno Cornelis; Gábor Fodor 002; Noelle Ocon; David B. Dunson; Ingrid Daubechies (SIAM J. Imaging Sci., 2016)

Bayesian network-response regression. - Lu Wang 015; Daniele Durante; Rex E. Jung; David B. Dunson (Bioinform., 2017)

Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics. - Yan Shang; David B. Dunson; Jing-Sheng Song 001 (Oper. Res., 2017)

Bayesian Tensor Regression. - Rajarshi Guhaniyogi; Shaan Qamar; David B. Dunson (J. Mach. Learn. Res., 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)

Extrema-weighted feature extraction for functional data. - Willem van den Boom; Callie Mao; Rebecca A. Schroeder; David B. Dunson (Bioinform., 2018)

Intrinsic Gaussian processes on complex constrained domains. - Mu Niu; Pokman Cheung; Lizhen Lin; Zhenwen Dai; Neil D. Lawrence; David B. Dunson (CoRR, 2018)

Reducing over-clustering via the powered Chinese restaurant process. - Jun Lu; Meng Li 002; David B. Dunson (CoRR, 2018)

Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks. - Shaobo Han; 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)

Clustering-Enhanced Stochastic Gradient MCMC for Hidden Markov Models with Rare States. - Rihui Ou; Alexander L. Young; David B. Dunson (CoRR, 2018)

Scaling up Data Augmentation MCMC via Calibration. - Leo L. Duan; James E. Johndrow; David B. Dunson (J. Mach. Learn. Res., 2018)

Scalable Bayes via Barycenter in Wasserstein Space. - Sanvesh Srivastava; Cheng Li; David B. Dunson (J. Mach. Learn. Res., 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)

Locally Convex Kernel Mixtures: Bayesian Subspace Learning. - Duy Hoang Thai; Hau-Tieng Wu; David B. Dunson (ICMLA, 2019)

Supervised Multiscale Dimension Reduction for Spatial Interaction Networks. - Shaobo Han; David B. Dunson (CoRR, 2019)

Classification via local manifold approximation. - Didong Li; David B. Dunson (CoRR, 2019)

Efficient Entropy Estimation for Stationary Time Series. - Alexander L. Young; David B. Dunson (CoRR, 2019)

Auto-encoding graph-valued data with applications to brain connectomes. - Meimei Liu; Zhengwu Zhang; David B. Dunson (CoRR, 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)

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

Symmetric Bilinear Regression for Signal Subgraph Estimation. - Lu Wang 015; Zhengwu Zhang; David B. Dunson (IEEE Trans. Signal Process., 2019)

Fiedler Regularization: Learning Neural Networks with Graph Sparsity. - Edric Tam; David B. Dunson (ICML, 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)

Reproducible Bootstrap Aggregating. - Meimei Liu; 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)

Bayesian neural networks and dimensionality reduction. - Deborshee Sen; Theodore Papamarkou; David B. Dunson (CoRR, 2020)

Bayesian Closed Surface Fitting Through Tensor Products. - Olivier Binette; Debdeep Pati; David B. Dunson (J. Mach. Learn. Res., 2020)

Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference. - Sean Plummer; Shuang Zhou 013; Anirban Bhattacharya; David B. Dunson; Debdeep Pati (AISTATS, 2021)

Gaussian Process Subspace Regression for Model Reduction. - Ruda Zhang; Simon Mak; David B. Dunson (CoRR, 2021)

Inferring Manifolds From Noisy Data Using Gaussian Processes. - David B. Dunson; Nan Wu (CoRR, 2021)

Monte Carlo Simulation on the Stiefel Manifold via Polar Expansion. - Michael Jauch; Peter D. Hoff; David B. Dunson (J. Comput. Graph. Stat., 2021)

Bayesian Distance Clustering. - Leo L. Duan; David B. Dunson (J. Mach. Learn. Res., 2021)

Soft Tensor Regression. - Georgia Papadogeorgou; Zhengwu Zhang; David B. Dunson (J. Mach. Learn. Res., 2021)

Bayesian time-aligned factor analysis of paired multivariate time series. - Arkaprava Roy; Jana Schaich Borg; David B. Dunson (J. Mach. Learn. Res., 2021)

Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets. - Meimei Liu; Zhengwu Zhang; David B. Dunson (NeuroImage, 2021)

Outlier detection for multi-network data. - Pritam Dey; Zhengwu Zhang; David B. Dunson (Bioinform., 2022)

Multiscale Graph Comparison via the Embedded Laplacian Distance. - Edric Tam; David B. Dunson (CoRR, 2022)

Spatial Multivariate Trees for Big Data Bayesian Regression. - Michele Peruzzi; David B. Dunson (J. Mach. Learn. Res., 2022)

Gaussian Process Subspace Prediction for Model Reduction. - Ruda Zhang; Simon Mak; David B. Dunson (SIAM J. Sci. Comput., 2022)

Exponential-Wrapped Distributions on Symmetric Spaces. - Emmanuel Chevallier; Didong Li; Yulong Lu; David B. Dunson (SIAM J. Math. Data Sci., 2022)

Spectral Gap Regularization of Neural Networks. - Edric Tam; 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)

Bayesian Transfer Learning. - Piotr M. Suder; Jason Xu; David B. Dunson (CoRR, 2023)

Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering. - Noirrit Kiran Chandra; Antonio Canale; David B. Dunson (J. Mach. Learn. Res., 2023)

Nearest Neighbor Dirichlet Mixtures. - Shounak Chattopadhyay; Antik Chakraborty; David B. Dunson (J. Mach. Learn. Res., 2023)

Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph. - Leo L. Duan; David B. Dunson (J. Mach. Learn. Res., 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)

PPA: Principal parcellation analysis for brain connectomes and multiple traits. - Rongjie Liu; Meng Li 002; David B. Dunson (NeuroImage, 2023)

Position: Bayesian Deep Learning is Needed 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; et al. (ICML, 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; et al. (CoRR, 2024)

Bayesian Joint Additive Factor Models for Multiview Learning. - Niccolo Anceschi; Federico Ferrari; David B. Dunson; Himel Mallick (CoRR, 2024)

Fast nonparametric feature selection with error control using integrated path stability selection. - Omar Melikechi; David B. Dunson; Jeffrey W. Miller (CoRR, 2024)

Spatial meshing for general Bayesian multivariate models. - Michele Peruzzi; David B. Dunson (J. Mach. Learn. Res., 2024)

Hierarchical Shrinkage Gaussian Processes: Applications to Computer Code Emulation and Dynamical System Recovery. - Tao Tang; Simon Mak; David B. Dunson (SIAM/ASA J. Uncertain. Quantification, 2024)

Ellipsoid Fitting With the Cayley Transform. - Omar Melikechi; David B. Dunson (IEEE Trans. Signal Process., 2024)

On the Statistical Capacity of Deep Generative Models. - Edric Tam; David B. Dunson (CoRR, 2025)

Accelerated algorithms for convex and non-convex optimization on manifolds. - Lizhen Lin; Bayan Saparbayeva; Michael Minyi Zhang; David B. Dunson (Mach. Learn., 2025)