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Rao-Blackwellized Stochastic Gradients for Discrete Distributions


Oct 10, 2018
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe

* 7 pages, 6 figures; submitted to AISTATS 2019 

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


Nov 05, 2017
Jeffrey Regier, Michael I. Jordan, Jon McAuliffe

* NIPS 2017 camera-ready 

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Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference


Nov 10, 2016
Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin Thomas, David Schlegel, Jon McAuliffe, Prabhat

* submitting to IPDPS'17 

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Celeste: Variational inference for a generative model of astronomical images


Jun 03, 2015
Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat

* in the Proceedings of the 32nd International Conference on Machine Learning (2015) 

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Variational inference for large-scale models of discrete choice


Jan 15, 2008
Michael Braun, Jon McAuliffe

* Journal of the American Statistical Association (2010) 105(489): 324-334 
* 29 pages, 2 tables, 2 figures 

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