Publications

Jeffrey Regier, Andrew Miller, David Schlegel, Ryan Adams, Jon McAuliffe, and Prabhat
Approximate inference for constructing astronomical catalogs from images.
In submission.
[pdf] [code]

Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin Thomas, and Prabhat.
Cataloging the visible universe through Bayesian inference at petascale.
International Parallel and Distributed Processing Symposium (IPDPS), 2018.
[pdf] [code]

Jeffrey Regier, Michael I. Jordan, and Jon McAuliffe.
Fast black-box variational inference through stochastic trust-region optimization.
Neural Informational Processing Systems (NIPS), 2017.
[pdf] [code]

Romain Lopez, Jeffrey Regier, Michael Jordan, and Nir Yosef.
A deep generative model for gene expression profiles from single-cell RNA sequencing.
Bay Area Machine Learning Symposium (BayLearn), 2017
[pdf] [code]

Jeffrey Regier and Jon McAuliffe.
Galaxy shape modeling with probabilistic auto-encoders.
In: A look at deep learning for science. Ed. by Prabhat. O'Reilly, 2017.
[text]

Jeffrey Regier and Jon McAuliffe.
Second-order stochastic variational inference.
Bay Area Machine Learning Symposium (BayLearn), 2016.
[pdf]

Jeffrey Regier and Philip B. Stark.
Mini-minimax uncertainty quantification for emulators.
SIAM/ASA Journal on Uncertainty Quantification, 2015.
[pdf] [code]

Jeffrey Regier, Jon McAuliffe, and Prabhat.
A deep generative model for astronomical images of galaxies.
Neural Informational Processing Systems (NIPS) Workshop: Advances in Approximate Bayesian Inference, 2015.
[pdf]

Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, and Prabhat.
Celeste: Variational inference for a generative model of astronomical images.
International Conference on Machine Learning (ICML), 2015.
[pdf] [code]

Andrew Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, and Ryan Adams.
A Gaussian process model of quasar spectral energy distributions.
Neural Information Processing Systems (NIPS), 2015.
[pdf]

Jeffrey Regier, Brenton Partridge, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, and Prabhat.
Celeste: Scalable variational inference for a generative model of astronomical images.
Neural Informational Processing Systems (NIPS) Workshop: Advances in Variational Inference. 2014.
[pdf] [code]

Jeffrey Regier and Uri Avissar.
System and method for retrieving and intelligently grouping definitions found in a repository of documents.
US Patent 7,747,555. June 2010.
[Google patents] [pdf]