7 citations · 7 across the 3 of their papers we have counts for
3 papers
stat.ME2016
Fast Measurements of Robustness to Changing Priors in Variational Bayes
Ryan Giordano, Tamara Broderick, Michael Jordan
In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. One hopes that the posterior is robust to reasonable variation in the choice of…
cs.DC2016★ 7 cited
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference
Jeffrey Regier, Kiran Pamnany, Ryan Giordano +4
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results. To date, Celeste has been scaled to at most hundreds of megabytes of as…
stat.ML2014
Covariance Matrices for Mean Field Variational Bayes
Ryan Giordano, Tamara Broderick
Mean Field Variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of…