25 citations · 31 across the 5 of their papers we have counts for
6 papers
Many processors, little time: MCMC for partitions via optimal transport couplings
Tin D. Nguyen, Brian L. Trippe, Tamara Broderick
Markov chain Monte Carlo (MCMC) methods are often used in clustering since they guarantee asymptotically exact expectations in the infinite-time limit. In finite time, though, slow…
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
Brian L. Trippe, Hilary K. Finucane, Tamara Broderick
Hierarchical Bayesian methods enable information sharing across multiple related regression problems. While standard practice is to model regression parameters (effects) as (1) exc…
Optimal transport couplings of Gibbs samplers on partitions for unbiased estimation
Brian L. Trippe, Tin D. Nguyen, Tamara Broderick
Computational couplings of Markov chains provide a practical route to unbiased Monte Carlo estimation that can utilize parallel computation. However, these approaches depend crucia…
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal +1
Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linea…
Conditional Density Estimation with Bayesian Normalising Flows
Brian L Trippe, Richard E Turner
Modeling complex conditional distributions is critical in a variety of settings. Despite a long tradition of research into conditional density estimation, current methods employ ei…
Overpruning in Variational Bayesian Neural Networks
Brian Trippe, Richard Turner
The motivations for using variational inference (VI) in neural networks differ significantly from those in latent variable models. This has a counter-intuitive consequence; more ex…