David B. Dunson

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 62

Top Venues

CoRR

J. Mach. Learn. Res.

Bioinform.

NIPS

Publications

Niccolo Anceschi, Federico Ferrari, David B. Dunson, Himel Mallick
Bayesian Joint Additive Factor Models for Multiview Learning. CoRR (2024)
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
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson
Machine Learning and the Future of Bayesian Computation. CoRR (2023)
Noirrit Kiran Chandra, Antonio Canale, David B. Dunson
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering. J. Mach. Learn. Res. 24 (2023)
Duy Hoang Thai, Alexander L. Young, David B. Dunson
Proximal Algorithms for Accelerated Langevin Dynamics. CoRR (2023)
Omar Melikechi, David B. Dunson
Ellipsoid fitting with the Cayley transform. CoRR (2023)
Edric Tam, David B. Dunson
Spectral Gap Regularization of Neural Networks. CoRR (2023)
Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson
Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data. J. Mach. Learn. Res. 24 (2023)
Tao Tang, Simon Mak, David B. Dunson
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery. CoRR (2023)
Rongjie Liu, Meng Li, David B. Dunson
PPA: Principal parcellation analysis for brain connectomes and multiple traits. NeuroImage 276 (2023)
Piotr M. Suder, Jason Xu, David B. Dunson
Bayesian Transfer Learning. CoRR (2023)
Michele Peruzzi, David B. Dunson
Spatial Multivariate Trees for Big Data Bayesian Regression. J. Mach. Learn. Res. 23 (2022)
Ruda Zhang, Simon Mak, David B. Dunson
Gaussian Process Subspace Prediction for Model Reduction. SIAM J. Sci. Comput. 44 (3) (2022)
Pritam Dey, Zhengwu Zhang, David B. Dunson
Outlier detection for multi-network data. Bioinform. 38 (16) (2022)
Emmanuel Chevallier, Didong Li, Yulong Lu, David B. Dunson
Exponential-Wrapped Distributions on Symmetric Spaces. SIAM J. Math. Data Sci. 4 (4) (2022)
Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson
Soft Tensor Regression. J. Mach. Learn. Res. 22 (2021)
Leo L. Duan, David B. Dunson
Bayesian Distance Clustering. J. Mach. Learn. Res. 22 (2021)
Sean Plummer, Shuang Zhou, Anirban Bhattacharya, David B. Dunson, Debdeep Pati
Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference. AISTATS (2021)
Michael Jauch, Peter D. Hoff, David B. Dunson
Monte Carlo Simulation on the Stiefel Manifold via Polar Expansion. J. Comput. Graph. Stat. 30 (3) (2021)
Arkaprava Roy, Jana Schaich Borg, David B. Dunson
Bayesian time-aligned factor analysis of paired multivariate time series. J. Mach. Learn. Res. 22 (2021)
David B. Dunson, Nan Wu
Inferring Manifolds From Noisy Data Using Gaussian Processes. CoRR (2021)
Meimei Liu, Zhengwu Zhang, David B. Dunson
Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets. NeuroImage 245 (2021)
Meimei Liu, David B. Dunson
Reproducible Bootstrap Aggregating. CoRR (2020)
Olivier Binette, Debdeep Pati, David B. Dunson
Bayesian Closed Surface Fitting Through Tensor Products. J. Mach. Learn. Res. 21 (2020)
Emanuele Aliverti, Jeffrey L. Tilson, Dayne L. Filer, Benjamin Babcock, Alejandro Colaneri, Jennifer Ocasio, Timothy R. Gershon, Kirk C. Wilhelmsen, David B. Dunson
Projected t-SNE for batch correction. Bioinform. 36 (11) (2020)
Sean Plummer, Shuang Zhou, Anirban Bhattacharya, David B. Dunson, Debdeep Pati
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference. CoRR (2020)
Lizhen Lin, Bayan Saparbayeva, Michael Minyi Zhang, David B. Dunson
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds. CoRR (2020)
Austin Talbot, David B. Dunson, Kafui Dzirasa, David E. Carlson
Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity. CoRR (2020)
Duy Hoang Thai, Hau-Tieng Wu, David B. Dunson
Locally Convex Kernel Mixtures: Bayesian Subspace Learning. ICMLA (2019)
Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu, David B. Dunson
Tensor network factorizations: Relationships between brain structural connectomes and traits. NeuroImage 197 (2019)
Meimei Liu, Zhengwu Zhang, David B. Dunson
Auto-encoding graph-valued data with applications to brain connectomes. CoRR (2019)
Shaobo Han, David B. Dunson
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks. CoRR (2019)
Didong Li, David B. Dunson
Classification via local manifold approximation. CoRR (2019)
Lu Wang, Zhengwu Zhang, David B. Dunson
Symmetric Bilinear Regression for Signal Subgraph Estimation. IEEE Trans. Signal Process. 67 (7) (2019)
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
Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease. Frontiers Neuroinformatics 13 (2019)
Alexander L. Young, David B. Dunson
Efficient Entropy Estimation for Stationary Time Series. CoRR (2019)
Leo L. Duan, James E. Johndrow, David B. Dunson
Scaling up Data Augmentation MCMC via Calibration. J. Mach. Learn. Res. 19 (2018)
Sanvesh Srivastava, Cheng Li, David B. Dunson
Scalable Bayes via Barycenter in Wasserstein Space. J. Mach. Learn. Res. 19 (2018)
Jun Lu, Meng Li, David B. Dunson
Reducing over-clustering via the powered Chinese restaurant process. CoRR (2018)
Willem van den Boom, Callie Mao, Rebecca A. Schroeder, David B. Dunson
Extrema-weighted feature extraction for functional data. Bioinform. 34 (14) (2018)
Shaobo Han, David B. Dunson
Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks. CoRR (2018)
Zhengwu Zhang, Maxime Descoteaux, Jingwen Zhang, Gabriel Girard, Maxime Chamberland, David B. Dunson, Anuj Srivastava, Hongtu Zhu
Mapping population-based structural connectomes. NeuroImage 172 (2018)
Leo L. Duan, David B. Dunson
Bayesian Distance Clustering. CoRR (2018)
Rihui Ou, Alexander L. Young, David B. Dunson
Clustering-Enhanced Stochastic Gradient MCMC for Hidden Markov Models with Rare States. CoRR (2018)
Jieren Xu, Yitong Li, David B. Dunson, Ingrid Daubechies, Haizhao Yang
Non-Oscillatory Pattern Learning for Non-Stationary Signals. CoRR (2018)
Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David B. Dunson
Intrinsic Gaussian processes on complex constrained domains. CoRR (2018)
Yan Shang, David B. Dunson, Jing-Sheng Song
Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics. Oper. Res. 65 (6) (2017)
Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson
Robust and Scalable Bayes via a Median of Subset Posterior Measures. J. Mach. Learn. Res. 18 (2017)
Lu Wang, Daniele Durante, Rex E. Jung, David B. Dunson
Bayesian network-response regression. Bioinform. 33 (12) (2017)
Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson
Bayesian Tensor Regression. J. Mach. Learn. Res. 18 (2017)
Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson
Boosting Variational Inference. CoRR (2016)
James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson
Inefficiency of Data Augmentation for Large Sample Imbalanced Data. CoRR (2016)
Kewei Tang, David B. Dunson, Zhixun Su, Risheng Liu, Jie Zhang, Jiangxin Dong
Subspace segmentation by dense block and sparse representation. Neural Networks 75 (2016)
Shiwen Zhao, Barbara E. Engelhardt, Sayan Mukherjee, David B. Dunson
Fast moment estimation for generalized latent Dirichlet models. CoRR (2016)
Rujie Yin, Bruno Cornelis, Gábor Fodor, Noelle Ocon, David B. Dunson, Ingrid Daubechies
Removing Cradle Artifacts in X-Ray Images of Paintings. SIAM J. Imaging Sci. 9 (3) (2016)
Xiangyu Wang, David B. Dunson, Chenlei Leng
No penalty no tears: Least squares in high-dimensional linear models. ICML (2016)
Ye Wang, Antonio Canale, David B. Dunson
Scalable geometric density estimation. AISTATS (2016)
Rajarshi Guhaniyogi, David B. Dunson
Compressed Gaussian Process for Manifold Regression. J. Mach. Learn. Res. 17 (2016)
Hongxiao Zhu, Nate Strawn, David B. Dunson
Bayesian Graphical Models for Multivariate Functional Data. J. Mach. Learn. Res. 17 (2016)
Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin
Variational Gaussian Copula Inference. AISTATS (2016)
Xiangyu Wang, David B. Dunson, Chenlei Leng
DECOrrelated feature space partitioning for distributed sparse regression. CoRR (2016)
Xiangyu Wang, David B. Dunson, Chenlei Leng
DECOrrelated feature space partitioning for distributed sparse regression. NIPS (2016)
Sanvesh Srivastava, Volkan Cevher, Quoc Tran-Dinh, David B. Dunson
WASP: Scalable Bayes via barycenters of subset posteriors. AISTATS (2015)
Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
Parallelizing MCMC with Random Partition Trees. NIPS (2015)
Fangjian Guo, David B. Dunson
Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach. RecSys (2015)
Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin
Variational Gaussian Copula Inference. CoRR (2015)
A. Yazdani, David B. Dunson
A hybrid bayesian approach for genome-wide association studies on related individuals. Bioinform. 31 (24) (2015)
Xiangyu Wang, David B. Dunson, Chenlei Leng
No penalty no tears: Least squares in high-dimensional linear models. CoRR (2015)
Ye Wang, David B. Dunson
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process. NIPS (2015)
Emily B. Fox, David B. Dunson
Bayesian nonparametric covariance regression. J. Mach. Learn. Res. 16 (2015)
Willem van den Boom, David B. Dunson, Galen Reeves
Quantifying uncertainty in variable selection with arbitrary matrices. CAMSAP (2015)
Xiangyu Wang, Chenlei Leng, David B. Dunson
On the consistency theory of high dimensional variable screening. NIPS (2015)
Xiangyu Wang, Chenlei Leng, David B. Dunson
On the consistency theory of high dimensional variable screening. CoRR (2015)
Xiangyu Wang, Peichao Peng, David B. Dunson
Median Selection Subset Aggregation for Parallel Inference. CoRR (2014)
David C. Kessler, Jack A. Taylor, David B. Dunson
Learning phenotype densities conditional on many interacting predictors. Bioinform. 30 (11) (2014)
Cathy W. S. Chen, David B. Dunson, Sylvia Frühwirth-Schnatter, Stephen G. Walker
Special issue on Bayesian computing, methods and applications. Comput. Stat. Data Anal. 71 (2014)
David E. Carlson, Joshua T. Vogelstein, Qisong Wu, Wenzhao Lian, Mingyuan Zhou, Colin R. Stoetzner, Daryl Kipke, Douglas Weber, David B. Dunson, Lawrence Carin
Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling. IEEE Trans. Biomed. Eng. 61 (1) (2014)
Rujie Yin, David B. Dunson, Bruno Cornelis, Bill Brown, Noelle Ocon, Ingrid Daubechies
Digital cradle removal in X-ray images of art paintings. ICIP (2014)
Xiangyu Wang, Peichao Peng, David B. Dunson
Median Selection Subset Aggregation for Parallel Inference. NIPS (2014)
Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson
Bayesian Conditional Density Filtering for Big Data. CoRR (2014)
Daniele Durante, David B. Dunson
Bayesian Logistic Gaussian Process Models for Dynamic Networks. AISTATS (2014)
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
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14). CoRR (2014)
Lauren A. Hannah, Warren B. Powell, David B. Dunson
Semiconvex Regression for Metamodeling-Based Optimization. SIAM J. Optim. 24 (2) (2014)
Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David B. Dunson, Lawrence Carin
Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors. ICML (2014)
Daniele Durante, Bruno Scarpa, David B. Dunson
Locally adaptive factor processes for multivariate time series. J. Mach. Learn. Res. 15 (1) (2014)
Sara Wade, David B. Dunson, Sonia Petrone, Lorenzo Trippa
Improving prediction from dirichlet process mixtures via enrichment. J. Mach. Learn. Res. 15 (1) (2014)
Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson
Robust and scalable Bayes via a median of subset posterior measures. CoRR (2014)
Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson
Scalable and Robust Bayesian Inference via the Median Posterior. ICML (2014)
Hongxia Yang, Fei Liu, Chunlin Ji, David B. Dunson
Adaptive sampling for Bayesian geospatial models. Stat. Comput. 24 (6) (2014)
James E. Johndrow, David B. Dunson, Kristian Lum
Diagonal Orthant Multinomial Probit Models. AISTATS (2013)
Bruno Cornelis, Yun Yang, Joshua T. Vogelstein, Ann Dooms, Ingrid Daubechies, David B. Dunson
Bayesian crack detection in ultra high resolution multimodal images of paintings CoRR (2013)
David C. Kessler, Jack A. Taylor, David B. Dunson
Learning Densities Conditional on Many Interacting Features CoRR (2013)
Eric F. Lock, David B. Dunson
Bayesian consensus clustering. Bioinform. 29 (20) (2013)
Rajarshi Guhaniyogi, David B. Dunson
Bayesian Compressed Regression CoRR (2013)
Bruno Cornelis, Yun Yang, Joshua T. Vogelstein, Ann Dooms, Ingrid Daubechies, David B. Dunson
Bayesian crack detection in ultra high resolution multimodal images of paintings. DSP (2013)
Bo Chen, Gungor Polatkan, Guillermo Sapiro, David M. Blei, David B. Dunson, Lawrence Carin
Deep Learning with Hierarchical Convolutional Factor Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 35 (8) (2013)
Francesca Petralia, Joshua T. Vogelstein, David B. Dunson
Multiscale Dictionary Learning for Estimating Conditional Distributions. NIPS (2013)
Eric F. Lock, David B. Dunson
Bayesian Consensus Clustering CoRR (2013)
Francesca Petralia, Joshua T. Vogelstein, David B. Dunson
Multiscale Dictionary Learning for Estimating Conditional Distributions. CoRR (2013)
Esther Salazar, David B. Dunson, Lawrence Carin
Analysis of space-time relational data with application to legislative voting. Comput. Stat. Data Anal. 68 (2013)

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Publication Activity (10 Years)

Top Topics

Top Venues

David B. Dunson