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Several recent works have explored stochastic gradient methods for variational inference that exploit the geometry of the variational-parameter space.
Generalized linear models
Nelder, John A and Baker, R Jacob · 1972
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Natural gradient works efficiently in learning
Amari, Shun-Ichi · 1998
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Beck, Amir and Teboulle, Marc · 2003
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Introductory Lectures on Convex Optimization: A Basic Course
Nesterov, Y · 2004
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Assessing approximate inference for binary Gaussian process classification
Kuss, M. and Rasmussen, C · 2005
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A correlated topic model of science
Blei, David M and Lafferty, John D · 2007
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A fast iterative shrinkage-thresholding algorithm with application to wavelet-based image deblurring
Beck, Amir and Teboulle, Marc · 2009
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Statistical exponential families: A digest with flash cards
Nielsen, Frank and Garcia, Vincent · 2009
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Composite objective mirror descent
Duchi, John C, Shalev-Shwartz, Shai, Singer, Yoram, and Tewari, Ambuj · 2010
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Message-passing for graph-structured linear programs: Proximal methods and rounding schemes
Ravikumar, Pradeep, Agarwal, Alekh, and Wainwright, Martin J · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, John, Hazan, Elad, and Singer, Yoram · 2011
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Approximate Riemannian conjugate gradient learning for fixed-form variational Bayes
Honkela, A., Raiko, T., Kuusela, M., Tornio, M., and Karhunen, J · 2011
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Lecture 6.5-RMSprop: Divide the gradient by a running average of its recent magnitude
Tieleman, Tijmen and Hinton, Geoffrey · 2012
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ADADELTA: An adaptive learning rate method
Zeiler, Matthew D · 2012
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Variational inference in nonconjugate models
Wang, Chong and Blei, David M · 2013
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Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
Ghadimi, Saeed, Lan, Guanghui, and Zhang, Hongchao · 2014
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Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
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Fully automatic variational inference of differentiable probability models
Kucukelbir, Alp, Ranganath, Rajesh, Gelman, Andrew, and Blei, David · 2014
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Neural variational inference and learning in belief networks
Mnih, Andriy and Gregor, Karol · 2014
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Hoffman, Matthew D, Blei, David M, Wang, Chong, and Paisley, John · 2013
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Revisiting natural gradient for deep networks
Pascanu, Razvan and Bengio, Yoshua · 2013
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Black box variational inference
Ranganath, Rajesh, Gerrish, Sean, and Blei, David M · 2013
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Fixed-form variational posterior approximation through stochastic linear regression
Salimans, Tim, Knowles, David A, et al · 2013
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On the convergence of stochastic variational inference in Bayesian networks
Paquet, Ulrich · 2014
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Doubly stochastic variational Bayes for non-conjugate inference
Titsias, Michalis and Lázaro-Gredilla, Miguel · 2014
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Kullback-Leibler Proximal Variational Inference
Khan, Mohammad Emtiyaz, Baque, Pierre, Flueret, Francois, and Fua, Pascal · 2015
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A trust-region method for stochastic variational inference with applications to streaming data
Theis, Lucas and Hoffman, Matthew D · 2015
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