research
∙
04/23/2020
Variance reduction for distributed stochastic gradient MCMC
Stochastic gradient MCMC methods, such as stochastic gradient Langevin d...
share
research
∙
07/31/2019
Scalable Bayesian Non-linear Matrix Completion
Matrix completion aims to predict missing elements in a partially observ...
research
∙
04/09/2019
Meta-analysis of Bayesian analyses
Meta-analysis aims to combine results from multiple related statistical ...
research
∙
03/11/2019
Embarrassingly parallel MCMC using deep invertible transformations
While MCMC methods have become a main work-horse for Bayesian inference,...
research
∙
03/02/2017
Distributed Bayesian Matrix Factorization with Minimal Communication
Bayesian matrix factorization (BMF) is a powerful tool for producing low...
research
∙
03/30/2016
Bayesian inference in hierarchical models by combining independent posteriors
Hierarchical models are versatile tools for joint modeling of data sets ...
research
∙
05/19/2015
Modelling-based experiment retrieval: A case study with gene expression clustering
Motivation: Public and private repositories of experimental data are gro...
research
∙
12/16/2014