4 citations · 10 across the 16 of their papers we have counts for
17 papers
Testing Additivity of Lead and Benzo[a]pyrene-induced Neurotoxicity in Caenorhabditis elegans Assays
Niccolo Anceschi, Javier Huayta, Joel N. Meyer +2
Exposure to environmental contaminants is a recognized cause of neurotoxicity, contributing to the onset of a broad range of neurological conditions. In realistic settings, such ex…
Leveraging ontologies to predict biological activity of chemicals across genes
Jennifer N. Kampe, David B. Dunson, Celeste K. Carberry +3
High-throughput screening (HTS) is useful for evaluating chemicals for potential human health risks. However, given the extraordinarily large number of genes, assay endpoints, and…
On the Posterior Computation Under the Dirichlet-Laplace Prior
Paolo Onorati, David B. Dunson, Antonio Canale
Modern applications routinely collect high-dimensional data, leading to statistical models having more parameters than there are samples available. A common solution is to impose s…
Local graph estimation with pathwise false discovery control
Omar Melikechi, David B. Dunson, Noureddine Melikechi +1
Many datasets include a small set of variables, such as biomarkers or clinical outcomes, whose relationships to the broader system are of primary scientific interest. Estimating th…
Nonparametric predictive inference for discrete data via Metropolis-adjusted Dirichlet sequences
Davide Agnoletto, Tommaso Rigon, David B. Dunson
This article is motivated by challenges in conducting Bayesian inferences on unknown discrete distributions, with a particular focus on count data. To avoid the computational disad…
Link prediction in ecological networks under extreme taxonomic bias
Jennifer N. Kampe, Camille M. M. DeSisto, David B. Dunson
Ecological networks offer powerful insights into community function, but without first characterizing these networks accurately, our ability to detect and interpret changes under e…
A Bayesian theory for estimation of biodiversity
Tommaso Rigon, Ching-Lung Hsu, David B. Dunson
Statistical inference on biodiversity has a rich history going back to RA Fisher. An influential ecological theory suggests the existence of a fundamental biodiversity number, deno…
Spectral decomposition-assisted multi-study factor analysis
Lorenzo Mauri, Niccolò Anceschi, David B. Dunson
This article focuses on covariance estimation for multi-study data. Popular approaches employ factor-analytic terms with shared and study-specific loadings that decompose the varia…
On the Statistical Capacity of Deep Generative Models
Edric Tam, David B. Dunson
Deep generative models are routinely used in generating samples from complex, high-dimensional distributions. Despite their apparent successes, their statistical properties are not…
Nonparametric IPSS: Fast, flexible feature selection with false discovery control
Omar Melikechi, David B. Dunson, Jeffrey W. Miller
Feature selection is a critical task in machine learning and statistics. However, existing feature selection methods either (i) rely on parametric methods such as linear or general…
Factor pre-training in Bayesian multivariate logistic models
Lorenzo Mauri, David B. Dunson
This article focuses on inference in logistic regression for high-dimensional binary outcomes. A popular approach induces dependence across the outcomes by including latent factors…
Exact Sampling of Spanning Trees via Fast-forwarded Random Walks
Edric Tam, David B. Dunson, Leo L. Duan
Tree graphs are routinely used in statistics. When estimating a Bayesian model with a tree component, sampling the posterior remains a core difficulty. Existing Markov chain Monte…
Blessing of dimension in Bayesian inference on covariance matrices
Shounak Chattopadhyay, Anru R. Zhang, David B. Dunson
Bayesian factor analysis is routinely used for dimensionality reduction in modeling of high-dimensional covariance matrices. Factor analytic decompositions express the covariance a…
Bayesian Transfer Learning
Piotr M. Suder, Jason Xu, David B. Dunson
Transfer learning is a burgeoning concept in statistical machine learning that seeks to improve inference and/or predictive accuracy on a domain of interest by leveraging data from…
Proximal Algorithms for Accelerated Langevin Dynamics
Duy H. Thai, Alexander L. Young, David B. Dunson
We develop a novel class of MCMC algorithms based on a stochastized Nesterov scheme. With an appropriate addition of noise, the result is a time-inhomogeneous underdamped Langevin…
Bayesian inference for generalized linear models via quasi-posteriors
Davide Agnoletto, Tommaso Rigon, David B. Dunson
Generalized linear models (GLMs) are routinely used for modeling relationships between a response variable and a set of covariates. The simple form of a GLM comes with easy interpr…
Sequential Gibbs Posteriors with Applications to Principal Component Analysis
Steven Winter, Omar Melikechi, David B. Dunson
Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter weighting information in the loss relative to th…