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Modern variational inference (VI) uses stochastic gradients to avoid intractable expectations, enabling large-scale probabilistic inference in complex models.
A stochastic approximation method
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Auto-encoding variational Bayes
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Particle Gibbs with ancestor sampling
F. Lindsten, M. I. Jordan, and T. B. Schön · 2014
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R. Ranganath, S. Gerrish, and D. Blei · 2014
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D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Reweighted wake-sleep
B. Bornschein and Y. Bengio · 2015
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Neural adaptive sequential Monte Carlo
S. S. Gu, Z. Ghahramani, and R. E. Turner · 2015
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Markov chain Monte Carlo and variational inference: Bridging the gap
Filtering variational objectives
C. J. Maddison, D. Lawson, G. Tucker, N. Heess, M. Norouzi, A. Mnih, A. Doucet, and Y. Whye Teh · 2017
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Reparameterization gradients through acceptance-rejection sampling algorithms
C. A. Naesseth, F. J. R. Ruiz, S. W. Linderman, and D. Blei · 2017
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Inference networks for sequential Monte Carlo in graphical models
H. Wu, H. Zimmermann, E. Sennesh, T. A. Le, and J.-W. van de Meent · 2017
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The measure and mismeasure of fairness: A critical review of fair machine learning
S. Corbett-Davies and S. Goel · 2018
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Twisted variational sequential Monte Carlo
D. Lawson, G. Tucker, C. A. Naesseth, C. Maddison, and Y. Whye Teh · 2018
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T. Salimans, D. Kingma, and M. Welling · 2015
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Patterns of scalable Bayesian inference
E. Angelino, M. J. Johnson, R. P. Adams, et al · 2016
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Importance weighted autoencoders
Y. Burda, R. Grosse, and R. Salakhutdinov · 2016
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Rényi divergence variational inference
Y. Li and R. E. Turner · 2016
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Inference networks for sequential Monte Carlo in graphical models
B. Paige and F. Wood · 2016
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The generalized reparameterization gradient
F. J. R. Ruiz, M. K. Titsias, and D. Blei · 2016
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Auto-encoding sequential Monte Carlo
T. A. Le, M. Igl, T. Rainforth, T. Jin, and F. Wood · 2018
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Graphical model inference: Sequential Monte Carlo meets deterministic approximations
F. Lindsten, J. Helske, and M. Vihola · 2018
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Variational sequential Monte Carlo
C. A. Naesseth, S. Linderman, R. Ranganath, and D. Blei · 2018
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Meta-learning MCMC proposals
T. Wang, Y. Wu, D. Moore, and S. J. Russell · 2018
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Reweighted expectation maximization
A. B. Dieng and J. Paisley · 2019
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Divide and couple: Using Monte Carlo variational objectives for posterior approximation
J. Domke and D. R. Sheldon · 2019
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On importance-weighted autoencoders
A. Finke and A. H. Thiery · 2019
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Auxiliary variational MCMC
R. Habib and D. Barber · 2019
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Neutra-lizing bad geometry in Hamiltonian Monte Carlo using neural transport
M. Hoffman, P. Sountsov, J. V. Dillon, I. Langmore, D. Tran, and S. Vasudevan · 2019
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Learning dynamical systems with particle stochastic approximation em
A. Lindholm and F. Lindsten · 2019
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A survey on bias and fairness in machine learning
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan · 2019
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Particle smoothing variational objectives
A. K. Moretti, Z. Wang, L. Wu, I. Drori, and I. Pe’er · 2019
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Elements of sequential Monte Carlo
C. A. Naesseth, F. Lindsten, and T. B. Schön · 2019
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A contrastive divergence for combining variational inference and MCMC
F. J. R. Ruiz and M. K. Titsias · 2019
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Joint stochastic approximation and its application to learning discrete latent variable models
Z. Ou and Y. Song · 2020
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