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ORCIDJon D. McAuliffe
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2020 – today
- 2023
[j6]Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, LSST Dark Energy Science Collaboration:
Variational Inference for Deblending Crowded Starfields. J. Mach. Learn. Res. 24: 179:1-179:36 (2023)- 2021
- [i7]Runjing Liu, Jon D. McAuliffe, Jeffrey Regier:
Variational Inference for Deblending Crowded Starfields. CoRR abs/2102.02409 (2021)
2010 – 2019
- 2019
- [j5]Jeffrey Regier
, Keno Fischer, Kiran Pamnany, Andreas Noack, Jarrett Revels, Maximilian Lam, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat:
Cataloging the visible universe through Bayesian inference in Julia at petascale. J. Parallel Distributed Comput. 127: 89-104 (2019) - [c8]Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML 2019: 4023-4031 - 2018
- [c7]Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat:
Cataloging the Visible Universe Through Bayesian Inference at Petascale. IPDPS 2018: 44-53 - [i6]Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat:
Cataloging the Visible Universe through Bayesian Inference at Petascale. CoRR abs/1801.10277 (2018) - [i5]Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat:
Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR abs/1803.00113 (2018) - [i4]Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. CoRR abs/1810.04777 (2018) - 2017
- [c6]Jeffrey Regier, Michael I. Jordan, Jon McAuliffe:
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. NIPS 2017: 2402-2411 - [i3]Jeffrey Regier, Michael I. Jordan, Jon McAuliffe:
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. CoRR abs/1706.02375 (2017) - 2016
- [i2]David M. Blei, Alp Kucukelbir, Jon D. McAuliffe:
Variational Inference: A Review for Statisticians. CoRR abs/1601.00670 (2016) - [i1]Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin C. Thomas, David Schlegel, Jon McAuliffe, Prabhat:
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference. CoRR abs/1611.03404 (2016) - 2015
- [j4]Hachem Saddiki, Jon McAuliffe, Patrick Flaherty:
GLAD: a mixed-membership model for heterogeneous tumor subtype classification. Bioinform. 31(2): 225-232 (2015) - [c5]Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat:
Celeste: Variational inference for a generative model of astronomical images. ICML 2015: 2095-2103 - [c4]Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams:
A Gaussian Process Model of Quasar Spectral Energy Distributions. NIPS 2015: 2494-2502
2000 – 2009
- 2008
- [c3]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: 193-200 - 2007
- [c2]David M. Blei, Jon D. McAuliffe:
Supervised Topic Models. NIPS 2007: 121-128 - 2006
- [j3]Jon D. McAuliffe, David M. Blei, Michael I. Jordan:
Nonparametric empirical Bayes for the Dirichlet process mixture model. Stat. Comput. 16(1): 5-14 (2006) - 2004
- [j2]Jon D. McAuliffe, Lior Pachter, Michael I. Jordan:
Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. Bioinform. 20(12): 1850-1860 (2004) - 2003
- [j1]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): 130-139 (2003) - [c1]Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe:
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS 2003: 1173-1180
Coauthor Index
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