Covariance Matrices for Mean Field Variational Bayes. - Ryan Giordano; Tamara Broderick (CoRR, 2014)

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes. - Ryan Giordano; Tamara Broderick; Michael I. Jordan (NIPS, 2015)

Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference. - Jeffrey Regier; Kiran Pamnany; Ryan Giordano; Rollin C. Thomas; David Schlegel; Jon McAuliffe; Prabhat (CoRR, 2016)

Cataloging the Visible Universe Through Bayesian Inference at Petascale. - Jeffrey Regier; Kiran Pamnany; Keno Fischer; Andreas Noack 001; Maximilian Lam; Jarrett Revels; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (IPDPS, 2018)

Covariances, Robustness, and Variational Bayes. - Ryan Giordano; Tamara Broderick; Michael I. Jordan (J. Mach. Learn. Res., 2018)

A Swiss Army Infinitesimal Jackknife. - Ryan Giordano; William T. Stephenson; Runjing Liu; Michael I. Jordan; Tamara Broderick (AISTATS, 2019)

A Higher-Order Swiss Army Infinitesimal Jackknife. - Ryan Giordano; Michael I. Jordan; Tamara Broderick (CoRR, 2019)

Cataloging the visible universe through Bayesian inference in Julia at petascale. - Jeffrey Regier; Keno Fischer; Kiran Pamnany; Andreas Noack 001; Jarrett Revels; Maximilian Lam; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (J. Parallel Distributed Comput., 2019)

Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. - Renato Berlinghieri; Brian L. Trippe; David R. Burt; Ryan James Giordano; Kaushik Srinivasan; Tamay M. Özgökmen; Junfei Xia; Tamara Broderick (ICML, 2023)

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. - Ryan Giordano; Martin Ingram; Tamara Broderick (J. Mach. Learn. Res., 2024)