3 papers
stat.ML2026
Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions
Joseph Feldman, Yuqi Gu
Deep generative models offer powerful tools for multivariate data analysis, but their black-box architectures are often unidentified and difficult to interpret. We introduce the De…
stat.ME2026
Differentially Private Bayesian Inference for Gaussian Copula Correlations
Shuo Wang, Joseph Feldman, Jerome P. Reiter
Gaussian copulas are widely used to estimate multivariate distributions and relationships. We present algorithms for estimating Gaussian copula correlations that ensure differentia…
stat.ME2025
Outcome-Assisted Multiple Imputation of Missing Treatments
Joseph Feldman, Jerome P. Reiter
We provide guidance on multiple imputation of missing at random treatments in observational studies. Specifically, analysts should account for both covariates and outcomes, i.e., n…