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David Brian Dunson
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- affiliation: Duke University, Department of Statistical Science
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Journal Articles
- 2023
[j59]Yuqi Gu
, Elena E. Erosheva, Gongjun Xu, David B. Dunson:
Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data. J. Mach. Learn. Res. 24: 88:1-88:49 (2023)- [j58]Noirrit Kiran Chandra, Antonio Canale, David B. Dunson:
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering. J. Mach. Learn. Res. 24: 144:1-144:42 (2023) - [j57]Rongjie Liu, Meng Li, David B. Dunson:
PPA: Principal parcellation analysis for brain connectomes and multiple traits. NeuroImage 276: 120214 (2023) - 2022
- [j56]Pritam Dey, Zhengwu Zhang, David B. Dunson:
Outlier detection for multi-network data. Bioinform. 38(16): 4011-4018 (2022) - [j55]Michele Peruzzi, David B. Dunson:
Spatial Multivariate Trees for Big Data Bayesian Regression. J. Mach. Learn. Res. 23: 17:1-17:40 (2022) - [j54]Ruda Zhang, Simon Mak, David B. Dunson:
Gaussian Process Subspace Prediction for Model Reduction. SIAM J. Sci. Comput. 44(3): 1428- (2022) - [j53]Emmanuel Chevallier, Didong Li, Yulong Lu, David B. Dunson:
Exponential-Wrapped Distributions on Symmetric Spaces. SIAM J. Math. Data Sci. 4(4): 1347-1368 (2022) - 2021
- [j52]Michael Jauch, Peter D. Hoff, David B. Dunson:
Monte Carlo Simulation on the Stiefel Manifold via Polar Expansion. J. Comput. Graph. Stat. 30(3): 622-631 (2021) - [j51]Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson:
Soft Tensor Regression. J. Mach. Learn. Res. 22: 219:1-219:53 (2021) - [j50]Leo L. Duan, David B. Dunson:
Bayesian Distance Clustering. J. Mach. Learn. Res. 22: 224:1-224:27 (2021) - [j49]Arkaprava Roy, Jana Schaich Borg, David B. Dunson:
Bayesian time-aligned factor analysis of paired multivariate time series. J. Mach. Learn. Res. 22: 250:1-250:27 (2021) - [j48]Meimei Liu, Zhengwu Zhang, David B. Dunson:
Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets. NeuroImage 245: 118750 (2021) - 2020
- [j47]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): 3522-3527 (2020) - [j46]Olivier Binette, Debdeep Pati, David B. Dunson:
Bayesian Closed Surface Fitting Through Tensor Products. J. Mach. Learn. Res. 21: 119:1-119:26 (2020) - 2019
- [j45]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: 72 (2019) - [j44]Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu, David B. Dunson:
Tensor network factorizations: Relationships between brain structural connectomes and traits. NeuroImage 197: 330-343 (2019) - [j43]Lu Wang, Zhengwu Zhang, David B. Dunson:
Symmetric Bilinear Regression for Signal Subgraph Estimation. IEEE Trans. Signal Process. 67(7): 1929-1940 (2019) - 2018
- [j42]Willem van den Boom, Callie Mao, Rebecca A. Schroeder, David B. Dunson:
Extrema-weighted feature extraction for functional data. Bioinform. 34(14): 2457-2464 (2018) - [j41]Sanvesh Srivastava, Cheng Li, David B. Dunson:
Scalable Bayes via Barycenter in Wasserstein Space. J. Mach. Learn. Res. 19: 8:1-8:35 (2018) - [j40]Leo L. Duan, James E. Johndrow, David B. Dunson:
Scaling up Data Augmentation MCMC via Calibration. J. Mach. Learn. Res. 19: 64:1-64:34 (2018) - [j39]Zhengwu Zhang, Maxime Descoteaux, Jingwen Zhang, Gabriel Girard, Maxime Chamberland, David B. Dunson, Anuj Srivastava, Hongtu Zhu:
Mapping population-based structural connectomes. NeuroImage 172: 130-145 (2018) - 2017
- [j38]Lu Wang, Daniele Durante, Rex E. Jung, David B. Dunson:
Bayesian network-response regression. Bioinform. 33(12): 1859-1866 (2017) - [j37]Yan Shang, David B. Dunson, Jing-Sheng Song:
Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics. Oper. Res. 65(6): 1574-1588 (2017) - [j36]Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson:
Bayesian Tensor Regression. J. Mach. Learn. Res. 18: 79:1-79:31 (2017) - [j35]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: 124:1-124:40 (2017) - 2016
- [j34]Rajarshi Guhaniyogi, David B. Dunson:
Compressed Gaussian Process for Manifold Regression. J. Mach. Learn. Res. 17: 69:1-69:26 (2016) - [j33]Hongxiao Zhu, Nate Strawn, David B. Dunson:
Bayesian Graphical Models for Multivariate Functional Data. J. Mach. Learn. Res. 17: 204:1-204:27 (2016) - [j32]Kewei Tang, David B. Dunson, Zhixun Su, Risheng Liu, Jie Zhang, Jiangxin Dong:
Subspace segmentation by dense block and sparse representation. Neural Networks 75: 66-76 (2016) - [j31]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): 1247-1272 (2016) - 2015
- [j30]A. Yazdani, David B. Dunson:
A hybrid bayesian approach for genome-wide association studies on related individuals. Bioinform. 31(24): 3890-3896 (2015) - [j29]Emily B. Fox, David B. Dunson:
Bayesian nonparametric covariance regression. J. Mach. Learn. Res. 16: 2501-2542 (2015) - 2014
- [j28]David C. Kessler, Jack A. Taylor, David B. Dunson:
Learning phenotype densities conditional on many interacting predictors. Bioinform. 30(11): 1562-1568 (2014) - [j27]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: 273 (2014) - [j26]Sara Wade, David B. Dunson, Sonia Petrone, Lorenzo Trippa:
Improving prediction from dirichlet process mixtures via enrichment. J. Mach. Learn. Res. 15(1): 1041-1071 (2014) - [j25]Daniele Durante, Bruno Scarpa, David B. Dunson:
Locally adaptive factor processes for multivariate time series. J. Mach. Learn. Res. 15(1): 1493-1522 (2014) - [j24]Hongxia Yang, Fei Liu, Chunlin Ji, David B. Dunson:
Adaptive sampling for Bayesian geospatial models. Stat. Comput. 24(6): 1101-1110 (2014) - [j23]Lauren A. Hannah, Warren B. Powell, David B. Dunson:
Semiconvex Regression for Metamodeling-Based Optimization. SIAM J. Optim. 24(2): 573-597 (2014) - [j22]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): 41-54 (2014) - 2013
- [j21]Eric F. Lock, David B. Dunson:
Bayesian consensus clustering. Bioinform. 29(20): 2610-2616 (2013) - [j20]Esther Salazar, David B. Dunson, Lawrence Carin:
Analysis of space-time relational data with application to legislative voting. Comput. Stat. Data Anal. 68: 141-154 (2013) - [j19]Lauren Hannah, David B. Dunson:
Multivariate convex regression with adaptive partitioning. J. Mach. Learn. Res. 14(1): 3261-3294 (2013) - [j18]Debdeep Pati, David B. Dunson, Surya T. Tokdar:
Posterior consistency in conditional distribution estimation. J. Multivar. Anal. 116: 456-472 (2013) - [j17]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): 1887-1901 (2013) - 2012
- [j16]Abhishek Bhattacharya, David B. Dunson:
Nonparametric Bayes classification and hypothesis testing on manifolds. J. Multivar. Anal. 111: 1-19 (2012) - [j15]Zhaowei Hua, David B. Dunson, John H. Gilmore, Martin Andreas Styner, Hongtu Zhu:
Semiparametric Bayesian local functional models for diffusion tensor tract statistics. NeuroImage 63(1): 460-474 (2012) - [j14]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:
High Dimensional Longitudinal Genomic Data: An analysis used for monitoring viral infections. IEEE Signal Process. Mag. 29(1): 108-123 (2012) - [j13]Mingyuan Zhou, Haojun Chen, John W. Paisley, Lu Ren, Lingbo Li, Zhengming Xing, David B. Dunson, Guillermo Sapiro, Lawrence Carin:
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images. IEEE Trans. Image Process. 21(1): 130-144 (2012) - 2011
- [j12]Lu Ren, Lan Du, Lawrence Carin, David B. Dunson:
Logistic Stick-Breaking Process. J. Mach. Learn. Res. 12: 203-239 (2011) - [j11]Chuanhua Xing, David B. Dunson:
Bayesian Inference for Genomic Data Integration Reduces Misclassification Rate in Predicting Protein-Protein Interactions. PLoS Comput. Biol. 7(7) (2011) - [j10]Lawrence Carin, Richard G. Baraniuk, Volkan Cevher, David B. Dunson, Michael I. Jordan, Guillermo Sapiro, Michael B. Wakin:
Learning Low-Dimensional Signal Models. IEEE Signal Process. Mag. 28(2): 39-51 (2011) - [j9]Garritt L. Page, David B. Dunson:
Bayesian Local Contamination Models for Multivariate Outliers. Technometrics 53(2): 152-162 (2011) - [j8]Minhua Chen, Jorge G. Silva, John W. Paisley, Chunping Wang, David B. Dunson, Lawrence Carin:
Corrections to "Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds". IEEE Trans. Signal Process. 59(3): 1329 (2011) - 2010
- [j7]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:
Bayesian Inference of the Number of Factors in Gene-Expression Analysis: Application to Human Virus Challenge Studies. BMC Bioinform. 11: 552 (2010) - [j6]Mingan Yang, David B. Dunson, Donna D. Baird:
Semiparametric Bayes hierarchical models with mean and variance constraints. Comput. Stat. Data Anal. 54(9): 2172-2186 (2010) - [j5]Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson:
Classification with Incomplete Data Using Dirichlet Process Priors. J. Mach. Learn. Res. 11: 3269-3311 (2010) - [j4]David M. Blei, Lawrence Carin, David B. Dunson:
Probabilistic Topic Models. IEEE Signal Process. Mag. 27(6): 55-65 (2010) - [j3]Minhua Chen, Jorge G. Silva, John W. Paisley, Chunping Wang, David B. Dunson, Lawrence Carin:
Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds. IEEE Trans. Signal Process. 58(12): 6140-6155 (2010) - 2009
- [j2]Shihao Ji, David B. Dunson, Lawrence Carin:
Multitask Compressive Sensing. IEEE Trans. Signal Process. 57(1): 92-106 (2009) - 2008
- [j1]Kai Ni, John W. Paisley, Lawrence Carin, David B. Dunson:
Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data. IEEE Trans. Signal Process. 56(8-2): 3918-3931 (2008)
Conference and Workshop Papers
- 2021
- [c49]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: 2449-2457 - 2020
- [c48]Edric Tam, David B. Dunson:
Fiedler Regularization: Learning Neural Networks with Graph Sparsity. ICML 2020: 9346-9355 - 2019
- [c47]Duy Hoang Thai, Hau-Tieng Wu, David B. Dunson:
Locally Convex Kernel Mixtures: Bayesian Subspace Learning. ICMLA 2019: 272-275 - 2016
- [c46]Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin:
Variational Gaussian Copula Inference. AISTATS 2016: 829-838 - [c45]Ye Wang, Antonio Canale, David B. Dunson:
Scalable geometric density estimation. AISTATS 2016: 857-865 - [c44]Xiangyu Wang, David B. Dunson, Chenlei Leng:
No penalty no tears: Least squares in high-dimensional linear models. ICML 2016: 1814-1822 - [c43]Xiangyu Wang, David B. Dunson, Chenlei Leng:
DECOrrelated feature space partitioning for distributed sparse regression. NIPS 2016: 802-810 - 2015
- [c42]Sanvesh Srivastava, Volkan Cevher, Quoc Tran-Dinh, David B. Dunson:
WASP: Scalable Bayes via barycenters of subset posteriors. AISTATS 2015 - [c41]Willem van den Boom, David B. Dunson, Galen Reeves:
Quantifying uncertainty in variable selection with arbitrary matrices. CAMSAP 2015: 385-388 - [c40]Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson:
Parallelizing MCMC with Random Partition Trees. NIPS 2015: 451-459 - [c39]Ye Wang, David B. Dunson:
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process. NIPS 2015: 1738-1746 - [c38]Xiangyu Wang, Chenlei Leng, David B. Dunson:
On the consistency theory of high dimensional variable screening. NIPS 2015: 2431-2439 - [c37]Fangjian Guo, David B. Dunson:
Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach. RecSys 2015: 317-320 - 2014
- [c36]Daniele Durante, David B. Dunson:
Bayesian Logistic Gaussian Process Models for Dynamic Networks. AISTATS 2014: 194-201 - [c35]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: 4299-4303 - [c34]Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson:
Scalable and Robust Bayesian Inference via the Median Posterior. ICML 2014: 1656-1664 - [c33]Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David B. Dunson, Lawrence Carin:
Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors. ICML 2014: 1800-1808 - [c32]Xiangyu Wang, Peichao Peng, David B. Dunson:
Median Selection Subset Aggregation for Parallel Inference. NIPS 2014: 2195-2203 - 2013
- [c31]Anjishnu Banerjee, Jared Murray, David B. Dunson:
Bayesian learning of joint distributions of objects. AISTATS 2013: 1-9 - [c30]James E. Johndrow, David B. Dunson, Kristian Lum:
Diagonal Orthant Multinomial Probit Models. AISTATS 2013: 29-38 - [c29]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: 1-8 - [c28]Daniele Durante, Bruno Scarpa, David B. Dunson:
Locally Adaptive Bayesian Multivariate Time Series. NIPS 2013: 1664-1672 - [c27]Francesca Petralia, Joshua T. Vogelstein, David B. Dunson:
Multiscale Dictionary Learning for Estimating Conditional Distributions. NIPS 2013: 1797-1805 - 2012
- [c26]Lauren Hannah, David B. Dunson:
Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design. ICML 2012 - [c25]Ivo Shterev, David B. Dunson:
Bayesian Watermark Attacks. ICML 2012 - [c24]Mingyuan Zhou, Lingbo Li, David B. Dunson, Lawrence Carin:
Lognormal and Gamma Mixed Negative Binomial Regression. ICML 2012 - [c23]Emily B. Fox, David B. Dunson:
Multiresolution Gaussian Processes. NIPS 2012: 746-754 - [c22]Francesca Petralia, Vinayak A. Rao, David B. Dunson:
Repulsive Mixtures. NIPS 2012: 1898-1906 - [c21]Alona Fyshe, Emily B. Fox, David B. Dunson, Tom M. Mitchell:
Hierarchical Latent Dictionaries for Models of Brain Activation. AISTATS 2012: 409-421 - [c20]Mingyuan Zhou, Lauren Hannah, David B. Dunson, Lawrence Carin:
Beta-Negative Binomial Process and Poisson Factor Analysis. AISTATS 2012: 1462-1471 - 2011
- [c19]Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David B. Dunson, Lawrence Carin:
Covariate-dependent dictionary learning and sparse coding. ICASSP 2011: 5824-5827 - [c18]Lauren Hannah, David B. Dunson:
Approximate Dynamic Programming for Storage Problems. ICML 2011: 337-344 - [c17]Bo Chen, Gungor Polatkan, Guillermo Sapiro, David B. Dunson, Lawrence Carin:
The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning. ICML 2011: 361-368 - [c16]Haojun Chen, David B. Dunson, Lawrence Carin:
Topic Modeling with Nonparametric Markov Tree. ICML 2011: 377-384 - [c15]XianXing Zhang, David B. Dunson, Lawrence Carin:
Tree-Structured Infinite Sparse Factor Model. ICML 2011: 785-792 - [c14]Artin Armagan, David B. Dunson, Merlise Clyde:
Generalized Beta Mixtures of Gaussians. NIPS 2011: 523-531 - [c13]Lu Ren, Yingjian Wang, David B. Dunson, Lawrence Carin:
The Kernel Beta Process. NIPS 2011: 963-971 - [c12]XianXing Zhang, David B. Dunson, Lawrence Carin:
Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices. NIPS 2011: 1395-1403 - [c11]Geoffrey J. Gordon, David B. Dunson:
Preface. AISTATS 2011: 1-2 - [c10]Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David B. Dunson, Lawrence Carin:
Dependent Hierarchical Beta Process for Image Interpolation and Denoising. AISTATS 2011: 883-891 - 2010
- [c9]Eric Wang, Dehong Liu, Jorge G. Silva, David B. Dunson, Lawrence Carin:
Joint Analysis of Time-Evolving Binary Matrices and Associated Documents. NIPS 2010: 2370-2378 - 2009
- [c8]Chunping Wang, Qi An, Lawrence Carin, David B. Dunson:
Multi-task classification with infinite local experts. ICASSP 2009: 1569-1572 - [c7]Lu Ren, David B. Dunson, Scott Lindroth, Lawrence Carin:
Music analysis with a Bayesian dynamic model. ICASSP 2009: 1681-1684 - [c6]Lan Du, Lu Ren, David B. Dunson, Lawrence Carin:
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation. NIPS 2009: 486-494 - 2008
- [c5]Qi An, Chunping Wang, Ivo Shterev, Eric Wang, Lawrence Carin, David B. Dunson:
Hierarchical kernel stick-breaking process for multi-task image analysis. ICML 2008: 17-24 - [c4]Yuting Qi, Dehong Liu, David B. Dunson, Lawrence Carin:
Multi-task compressive sensing with Dirichlet process priors. ICML 2008: 768-775 - [c3]Lu Ren, David B. Dunson, Lawrence Carin:
The dynamic hierarchical Dirichlet process. ICML 2008: 824-831 - 2007
- [c2]Kai Ni, Lawrence Carin, David B. Dunson:
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process. ICML 2007: 689-696 - [c1]Ya Xue, David B. Dunson, Lawrence Carin:
The matrix stick-breaking process for flexible multi-task learning. ICML 2007: 1063-1070
Editorship
- 2011
- [e1]Geoffrey J. Gordon, David B. Dunson, Miroslav Dudík:
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011. JMLR Proceedings 15, JMLR.org 2011 [contents]
Informal and Other Publications
- 2024
- [i45]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 abs/2402.00809 (2024) - 2023
- [i44]Tao Tang, Simon Mak, David B. Dunson:
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery. CoRR abs/2302.00755 (2023) - [i43]Edric Tam, David B. Dunson:
Spectral Gap Regularization of Neural Networks. CoRR abs/2304.03096 (2023) - [i42]Omar Melikechi, David B. Dunson:
Ellipsoid fitting with the Cayley transform. CoRR abs/2304.10630 (2023) - [i41]Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson:
Machine Learning and the Future of Bayesian Computation. CoRR abs/2304.11251 (2023) - [i40]Piotr M. Suder, Jason Xu, David B. Dunson:
Bayesian Transfer Learning. CoRR abs/2312.13484 (2023) - 2022
- [i39]Edric Tam, David B. Dunson:
Multiscale Graph Comparison via the Embedded Laplacian Distance. CoRR abs/2201.12064 (2022) - 2021
- [i38]Ruda Zhang, Simon Mak, David B. Dunson:
Gaussian Process Subspace Regression for Model Reduction. CoRR abs/2107.04668 (2021) - [i37]David B. Dunson, Nan Wu:
Inferring Manifolds From Noisy Data Using Gaussian Processes. CoRR abs/2110.07478 (2021) - 2020
- [i36]Meimei Liu, David B. Dunson:
Reproducible Bootstrap Aggregating. CoRR abs/2001.03988 (2020) - [i35]Edric Tam, David B. Dunson:
Fiedler Regularization: Learning Neural Networks with Graph Sparsity. CoRR abs/2003.00992 (2020) - [i34]Austin Talbot, David B. Dunson, Kafui Dzirasa, David E. Carlson:
Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity. CoRR abs/2004.05209 (2020) - [i33]Deborshee Sen, Theodore Papamarkou, David B. Dunson:
Bayesian neural networks and dimensionality reduction. CoRR abs/2008.08044 (2020) - [i32]Lizhen Lin, Bayan Saparbayeva, Michael Minyi Zhang, David B. Dunson:
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds. CoRR abs/2010.08908 (2020) - [i31]Sean Plummer, Shuang Zhou, Anirban Bhattacharya, David B. Dunson, Debdeep Pati:
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference. CoRR abs/2010.14056 (2020) - 2019
- [i30]Shaobo Han, David B. Dunson:
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks. CoRR abs/1901.00172 (2019) - [i29]Didong Li, David B. Dunson:
Classification via local manifold approximation. CoRR abs/1903.00985 (2019) - [i28]Alexander L. Young, David B. Dunson:
Efficient Entropy Estimation for Stationary Time Series. CoRR abs/1904.05850 (2019) - [i27]Meimei Liu, Zhengwu Zhang, David B. Dunson:
Auto-encoding graph-valued data with applications to brain connectomes. CoRR abs/1911.02728 (2019) - 2018
- [i26]Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David B. Dunson:
Intrinsic Gaussian processes on complex constrained domains. CoRR abs/1801.01061 (2018) - [i25]Jun Lu, Meng Li, David B. Dunson:
Reducing over-clustering via the powered Chinese restaurant process. CoRR abs/1802.05392 (2018) - [i24]Shaobo Han, David B. Dunson:
Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks. CoRR abs/1803.01203 (2018) - [i23]Jieren Xu, Yitong Li, David B. Dunson, Ingrid Daubechies, Haizhao Yang:
Non-Oscillatory Pattern Learning for Non-Stationary Signals. CoRR abs/1805.08102 (2018) - [i22]Leo L. Duan, David B. Dunson:
Bayesian Distance Clustering. CoRR abs/1810.08537 (2018) - [i21]Rihui Ou, Alexander L. Young, David B. Dunson:
Clustering-Enhanced Stochastic Gradient MCMC for Hidden Markov Models with Rare States. CoRR abs/1810.13431 (2018) - 2016
- [i20]Xiangyu Wang, David B. Dunson, Chenlei Leng:
DECOrrelated feature space partitioning for distributed sparse regression. CoRR abs/1602.02575 (2016) - [i19]Shiwen Zhao, Barbara E. Engelhardt, Sayan Mukherjee, David B. Dunson:
Fast moment estimation for generalized latent Dirichlet models. CoRR abs/1603.05324 (2016) - [i18]James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson:
Inefficiency of Data Augmentation for Large Sample Imbalanced Data. CoRR abs/1605.05798 (2016) - [i17]Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson:
Boosting Variational Inference. CoRR abs/1611.05559 (2016) - 2015
- [i16]Xiangyu Wang, Chenlei Leng, David B. Dunson:
On the consistency theory of high dimensional variable screening. CoRR abs/1502.06895 (2015) - [i15]Xiangyu Wang, David B. Dunson, Chenlei Leng:
No penalty no tears: Least squares in high-dimensional linear models. CoRR abs/1506.02222 (2015) - [i14]Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin:
Variational Gaussian Copula Inference. CoRR abs/1506.05860 (2015) - 2014
- [i13]Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson:
Bayesian Conditional Density Filtering for Big Data. CoRR abs/1401.3632 (2014) - [i12]Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson:
Robust and scalable Bayes via a median of subset posterior measures. CoRR abs/1403.2660 (2014) - [i11]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 abs/1410.0719 (2014) - [i10]Xiangyu Wang, Peichao Peng, David B. Dunson:
Median Selection Subset Aggregation for Parallel Inference. CoRR abs/1410.6604 (2014) - 2013
- [i9]Eric F. Lock, David B. Dunson:
Bayesian Consensus Clustering. CoRR abs/1302.7280 (2013) - [i8]Rajarshi Guhaniyogi, David B. Dunson:
Bayesian Compressed Regression. CoRR abs/1303.0642 (2013) - [i7]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 abs/1304.5894 (2013) - [i6]David C. Kessler, Jack A. Taylor, David B. Dunson:
Learning Densities Conditional on Many Interacting Features. CoRR abs/1304.7230 (2013) - [i5]Francesca Petralia, Joshua T. Vogelstein, David B. Dunson:
Multiscale Dictionary Learning for Estimating Conditional Distributions. CoRR abs/1312.1099 (2013) - [i4]Xiangyu Wang, David B. Dunson:
Parallel MCMC via Weierstrass Sampler. CoRR abs/1312.4605 (2013) - 2012
- [i3]Lauren Hannah, David B. Dunson:
Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design. CoRR abs/1206.4645 (2012) - [i2]Ivo Shterev, David B. Dunson:
Bayesian Watermark Attacks. CoRR abs/1206.4662 (2012) - [i1]Mingyuan Zhou, Lingbo Li, David B. Dunson, Lawrence Carin:
Lognormal and Gamma Mixed Negative Binomial Regression. CoRR abs/1206.6456 (2012)
Coauthor Index
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