David B. Dunson
34 papers · 2009–2024 · 2 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Interdisciplinary Bridge π Renaissance Researcher (8) πΊοΈ Taxonomy Completionist (20) π£ Hot Topic Early Bird
π
Conference Polyglot
(2)
π
Academic Marathon
(15)
π
Cross-Pollinator
(15)
π
Conference Loyalist
(20)
π¬
Deep Specialist
(14)
π±
Topic Pioneer
π
Keyword Champion
(10)
π
Century Club
(34)
ποΈ
Keyword Collector
(115)
π₯
Unstoppable
(10)
π
Trend Setter
Conferences
JMLR (20)
NIPS (14)
Top co-authors
Research topics
Keywords
bayesian inference
(21)
markov chain monte carlo
(8)
gaussian process
(7)
bayesian nonparametrics
(7)
variational inference
(5)
dirichlet process
(5)
posterior distribution
(3)
mixture model
(3)
density estimation
(3)
nonparametric bayesian
(3)
nonparametric regression
(2)
dimensionality reduction
(2)
probabilistic modeling
(2)
bayesian regression
(2)
variational bayesian
(2)
non-parametric bayesian
(2)
covariance estimation
(2)
high-dimensional statistics
(2)
gaussian processes
(2)
feature selection
(2)
Papers
Spatial meshing for general Bayesian multivariate models
JMLR 2024
Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph
JMLR 2023
Nearest Neighbor Dirichlet Mixtures
JMLR 2023
Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering
JMLR 2023
Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data
JMLR 2023
Spatial Multivariate Trees for Big Data Bayesian Regression
JMLR 2022
Soft Tensor Regression
JMLR 2021
Bayesian Distance Clustering
JMLR 2021
Bayesian Closed Surface Fitting Through Tensor Products
JMLR 2020
Scaling up Data Augmentation MCMC via Calibration
JMLR 2018
Scalable Bayes via Barycenter in Wasserstein Space
JMLR 2018
Robust and Scalable Bayes via a Median of Subset Posterior Measures
JMLR 2017
Bayesian Tensor Regression
JMLR 2017
Bayesian Graphical Models for Multivariate Functional Data
JMLR 2016
DECOrrelated feature space partitioning for distributed sparse regression
NIPS 2016
Compressed Gaussian Process for Manifold Regression
JMLR 2016
Parallelizing MCMC with Random Partition Trees
NIPS 2015
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
NIPS 2015
Bayesian Nonparametric Covariance Regression
JMLR 2015
On the consistency theory of high dimensional variable screening
NIPS 2015
Locally Adaptive Factor Processes for Multivariate Time Series
JMLR 2014
Median Selection Subset Aggregation for Parallel Inference
NIPS 2014
Improving Prediction from Dirichlet Process Mixtures via Enrichment
JMLR 2014
Multivariate Convex Regression with Adaptive Partitioning
JMLR 2013
Locally Adaptive Bayesian Multivariate Time Series
NIPS 2013
Multiscale Dictionary Learning for Estimating Conditional Distributions
NIPS 2013
Repulsive Mixtures
NIPS 2012
Multiresolution Gaussian Processes
NIPS 2012
Generalized Beta Mixtures of Gaussians
NIPS 2011
The Kernel Beta Process
NIPS 2011
Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices
NIPS 2011
Joint Analysis of Time-Evolving Binary Matrices and Associated Documents
NIPS 2010
Classification with Incomplete Data Using Dirichlet Process Priors
JMLR 2010
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation
NIPS 2009