Fetching the paper…
Reading the bibliography…
We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class.
Non-parametric estimation of a multivariate probability density
V. A. Epanechnikov · 1967
Earlier work this paper cites.
Data analysis using Stein’s estimator and its generalizations
Bradley Efron and Carl Morris · 1975
Earlier work this paper cites.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
Earlier work this paper cites.
Matrix Algebra from a Statistician’s Perspective
D. A. Harville · 1997
Earlier work this paper cites.
Improving the mean field approximation via the use of mixture distributions
Tommi S Jaakkola and Michael I Jordan · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
Earlier work this paper cites.
Estimation of Mixture Models
Q. Li · 1999
Earlier work this paper cites.
Mixture density estimation
Q. J. Li and A. R. Barron · 1999
Earlier work this paper cites.
Greedy function approximation: A gradient boosting machine
J. H. Friedman · 2000
Earlier work this paper cites.
Sequential greedy approximation for certain convex optimization problems
T. Zhang · 2003
Earlier work this paper cites.
Pattern recognition and machine learning, 2006
C Bishop · 2006
Earlier work this paper cites.
Data analysis using regression and multilevel/hierarchical models
Andrew Gelman and Jennifer Hill · 2006
Earlier work this paper cites.
Risk bounds for mixture density estimation
A. Rakhlin, Panchenko D., and Mukherjee S · 2006
Earlier work this paper cites.
An analysis of the nypd’s stop-and-frisk policy in the context of claims of racial bias
Andrew Gelman, Jeffrey Fagan, Alex Kiss, et al · 2007
Cited alongside, same era.
Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael I Jordan · 2008
Cited alongside, same era.
Gaussian covariance and scalable variational inference
M. W. Seeger · 2010
Cited alongside, same era.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
Cited alongside, same era.
Nonparametric variational inference
Samuel Gershman, Matt Hoffman, and David M Blei · 2012
Cited alongside, same era.
Matrix Computations
G. H. Golub and C. F. Van Loan · 2013
Cited alongside, same era.
Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David M Blei · 2014
Later among the works it cites.
Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan P Adams · 2015
Later among the works it cites.
Autograd: Reverse-mode differentiation of native python
Dougal Maclaurin, David Duvenaud, and Ryan P. Adams · 2015
Later among the works it cites.
Autograd: Reverse-mode differentiation of native Python, 2015
Dougal Maclaurin, David Duvenaud, Matthew Johnson, and Ryan P. Adams · 2015
Later among the works it cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Later among the works it cites.
Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John William Paisley · 2013
Cited alongside, same era.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Cited alongside, same era.
Fixed-form variational posterior approximation through stochastic linear regression
Tim Salimans, David A Knowles, et al · 2013
Cited alongside, same era.
The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
Matthew D Hoffman and Andrew Gelman · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Firefly monte carlo: Exact mcmc with subsets of data
Dougal Maclaurin and Ryan P Adams · 2014
Cited alongside, same era.
Closest in time.
Boosting variational inference
Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, and David B. Dunson · 2016
Closest in time.
Composing graphical models with neural networks for structured representations and fast inference
Matthew J. Johnson, David K. Duvenaud, Alex B. Wiltschko, Sandeep R. Datta, and Ryan P. Adams · 2016
Closest in time.
Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
Closest in time.
Hierarchical variational models
Rajesh Ranganath, Dustin Tran, and David M Blei · 2016
Closest in time.
Adagan: Boosting generative models
I. Tolstikhin, S. Gelly, O. Bousquet, C.-J. Simon-Gabriel, and B. Schoelkopf · 2016
Closest in time.
Gaussian variational approximation with factor covariance structure
V. M.-H. Ong, D. J. Nott, and M. S. Smith · 2017
Closest in time.