Jon D. McAuliffe

According to our database, Jon D. McAuliffe authored at least 18 papers between 2003 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Variational Inference for Deblending Crowded Starfields.

Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, LSST Dark Energy Science Collaboration

J. Mach. Learn. Res., 2023

2021

Variational Inference for Deblending Crowded Starfields.

Runjing Liu, Jon D. McAuliffe, Jeffrey Regier

CoRR, 2021

2019

Cataloging the visible universe through Bayesian inference in Julia at petascale.

Jeffrey Regier, Keno Fischer, Kiran Pamnany, Andreas Noack, Jarrett Revels, Maximilian Lam, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat

J. Parallel Distributed Comput., 2019

Rao-Blackwellized Stochastic Gradients for Discrete Distributions.

Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Cataloging the Visible Universe Through Bayesian Inference at Petascale.

Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat

Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium, 2018

2017

Fast Black-box Variational Inference through Stochastic Trust-Region Optimization.

Jeffrey Regier, Michael I. Jordan, Jon McAuliffe

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

Variational Inference: A Review for Statisticians.

David M. Blei, Alp Kucukelbir, Jon D. McAuliffe

CoRR, 2016

2015

GLAD: a mixed-membership model for heterogeneous tumor subtype classification.

Hachem Saddiki, Jon McAuliffe, Patrick Flaherty

Bioinform., 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

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

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

Proceedings of the 32nd International Conference on Machine Learning, 2015

2008

A spatially varying two-sample recombinant coalescent, with applications to HIV escape response.

Alexander Braunstein, Zhi Wei, Shane T. Jensen, Jon D. McAuliffe

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

Supervised Topic Models.

David M. Blei, Jon D. McAuliffe

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006

Nonparametric empirical Bayes for the Dirichlet process mixture model.

Jon D. McAuliffe, David M. Blei, Michael I. Jordan

Stat. Comput., 2006

2004

Multiple-sequence functional annotation and the generalized hidden Markov phylogeny.

Jon D. McAuliffe, Lior Pachter, Michael I. Jordan

Bioinform., 2004

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

Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates.

Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003