Jon D. McAuliffe

Publication Activity (10 Years)

Years Active: 2003-2023
Publications (10 Years): 5

Top Topics

Probabilistic Graphical Models

Posterior Distribution

Gaussian Process

Latent Dirichlet Allocation

Top Venues

J. Mach. Learn. Res.

Publications

Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, LSST Dark Energy Science Collaboration
Variational Inference for Deblending Crowded Starfields. J. Mach. Learn. Res. 24 (2023)
Runjing Liu, Jon D. McAuliffe, Jeffrey Regier
Variational Inference for Deblending Crowded Starfields. CoRR (2021)
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML (2019)
Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat
Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR (2018)
David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
Variational Inference: A Review for Statisticians. CoRR (2016)
Alexander Braunstein, Zhi Wei, Shane T. Jensen, Jon D. McAuliffe
A spatially varying two-sample recombinant coalescent, with applications to HIV escape response. NIPS (2008)
David M. Blei, Jon D. McAuliffe
Supervised Topic Models. NIPS (2007)
Jon D. McAuliffe, David M. Blei, Michael I. Jordan
Nonparametric empirical Bayes for the Dirichlet process mixture model. Stat. Comput. 16 (1) (2006)
Jon D. McAuliffe, Lior Pachter, Michael I. Jordan
Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. Bioinform. 20 (12) (2004)
Benjamin I. P. Rubinstein, Jon D. McAuliffe, Simon Cawley, Marimuthu Palaniswami, Kotagiri Ramamohanarao, Terence P. Speed
Machine learning in low-level microarray analysis. SIGKDD Explor. 5 (2) (2003)
Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS (2003)