Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. - Peter L. Bartlett; Michael I. Jordan; Jon D. McAuliffe (NIPS, 2003)

Machine learning in low-level microarray analysis. - Benjamin I. P. Rubinstein; Jon D. McAuliffe; Simon Cawley; Marimuthu Palaniswami; Kotagiri Ramamohanarao; Terence P. Speed (SIGKDD Explor., 2003)

Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. - Jon D. McAuliffe; Lior Pachter; Michael I. Jordan (Bioinform., 2004)

Nonparametric empirical Bayes for the Dirichlet process mixture model. - Jon D. McAuliffe; David M. Blei; Michael I. Jordan (Stat. Comput., 2006)

Supervised Topic Models. - David M. Blei; Jon D. McAuliffe (NIPS, 2007)

A spatially varying two-sample recombinant coalescent, with applications to HIV escape response. - Alexander Braunstein; Zhi Wei 001; Shane T. Jensen; Jon D. McAuliffe (NIPS, 2008)

Celeste: Variational inference for a generative model of astronomical images. - Jeffrey Regier; Andrew C. Miller; Jon McAuliffe; Ryan P. Adams; Matthew D. Hoffman; Dustin Lang; David Schlegel; Prabhat (ICML, 2015)

A Gaussian Process Model of Quasar Spectral Energy Distributions. - Andrew C. Miller; Albert Wu; Jeffrey Regier; Jon McAuliffe; Dustin Lang; Prabhat; David Schlegel; Ryan P. Adams (NIPS, 2015)

GLAD: a mixed-membership model for heterogeneous tumor subtype classification. - Hachem Saddiki; Jon McAuliffe; Patrick Flaherty (Bioinform., 2015)

Variational Inference: A Review for Statisticians. - David M. Blei; Alp Kucukelbir; Jon D. McAuliffe (CoRR, 2016)

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)

Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. - Jeffrey Regier; Michael I. Jordan; Jon McAuliffe (NIPS, 2017)

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)

Approximate Inference for Constructing Astronomical Catalogs from Images. - Jeffrey Regier; Andrew C. Miller; David Schlegel; Ryan P. Adams; Jon D. McAuliffe; Prabhat (CoRR, 2018)

Rao-Blackwellized Stochastic Gradients for Discrete Distributions. - Runjing Liu; Jeffrey Regier; Nilesh Tripuraneni; Michael I. Jordan; Jon D. McAuliffe (ICML, 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)

Variational Inference for Deblending Crowded Starfields. - Runjing Liu; Jon D. McAuliffe; Jeffrey Regier (CoRR, 2021)

Variational Inference for Deblending Crowded Starfields. - Runjing Liu; Jon D. McAuliffe; Jeffrey Regier; LSST Dark Energy Science Collaboration (J. Mach. Learn. Res., 2023)