Publications

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]

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.
[pdf]

Jeffrey Regier and Jon McAuliffe.
Second-Order Stochastic Variational Inference.
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]