Jon D. McAuliffe

11 papers A* 1Journal 7Unranked 3
YearRankTypeTitle / Venue / Authors
2023 J jnl
Variational Inference for Deblending Crowded Starfields.
J. Mach. Learn. Res.
Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, LSST Dark Energy Science Collaboration
2021 J jnl
Variational Inference for Deblending Crowded Starfields.
CoRR
Runjing Liu, Jon D. McAuliffe, Jeffrey Regier
2019 A* conf
Rao-Blackwellized Stochastic Gradients for Discrete Distributions.
ICML
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe
2018 J jnl
Approximate Inference for Constructing Astronomical Catalogs from Images.
CoRR
Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat
2016 J jnl
Variational Inference: A Review for Statisticians.
CoRR
David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
2008 — conf
A spatially varying two-sample recombinant coalescent, with applications to HIV escape response.
NIPS
Alexander Braunstein, Zhi Wei, Shane T. Jensen, Jon D. McAuliffe
2007 — conf
Supervised Topic Models.
NIPS
David M. Blei, Jon D. McAuliffe
2006 J jnl
Nonparametric empirical Bayes for the Dirichlet process mixture model.
Stat. Comput.
Jon D. McAuliffe, David M. Blei, Michael I. Jordan
2004 J jnl
Multiple-sequence functional annotation and the generalized hidden Markov phylogeny.
Bioinform.
Jon D. McAuliffe, Lior Pachter, Michael I. Jordan
2003 — conf
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates.
NIPS
Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe
2003 J jnl
Machine learning in low-level microarray analysis.
SIGKDD Explor.
Benjamin I. P. Rubinstein, Jon D. McAuliffe, Simon Cawley, Marimuthu Palaniswami, Kotagiri Ramamohanarao, Terence P. Speed