4 citations · 10 across the 16 of their papers we have counts for
4 papers · 1 filter
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…