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Statistical theory / M-estimators / Operations research / Variational Bayesian methods / Mathematical optimization / Stochastic gradient descent / Stochastic optimization / Variance / Bias of an estimator / Statistics / Probability theory / Estimation theory


Smoothed Gradients for Stochastic Variational Inference David Blei Department of Computer Science Department of Statistics Columbia University
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Document Date: 2015-03-12 00:16:22


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SIAM Journal / the New York Times / Neural Information Processing Systems / /

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set 2000 / /

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Columbia University / National Science Foundation / Alfred P. Sloan / Stochastic Variational Inference David Blei Department of Computer Science Department / DARPA FA8750-14-2-0009 / Princeton University / Princeton Center for Complex Materials Fellowship / National Academy of Sciences / /

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Yurii Nesterov / Christopher M Bishop / Jake M Hofman / James Hensman / Rie Johnson / Paul Tseng / Francis R Bach / Mirwaes Wahabzada / Marius Kloft / Nicolo Fusi / Tong Zhang / Neil Houlsby / Mark Schmidt / Rajesh Ranganath / Prem K Gopalan / Michael I Jordan / Matthew Hoffman / Stephen E Fienberg / Edoardo M Airoldi / Eric P Xing / Kristian Kersting / Aki Sato / Xi Chen / John Paisley / Andrew Y Ng / Laurent Charlin / Linpeng Tang / Alex Smola / Nicolas Le Roux / Prem Gopalan / Prem Gopolan / Chong Wang / Neil D Lawrence / Herbert Robbins / Matthew D Hoffman / David M Blei / Francis Bach / Sutton Monro / Stephan Mandt / /

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Proceedings of the National Academy of Sciences / the New York Times / Machine Learning / The Journal of Machine Learning Research / New York Times / /

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gradient averaging Our algorithm / 15 16 17 18 19 20 21 22 23 Algorithm / Machine Learning / /

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