Hierarchical Implicit Models and Likelihood-Free Variational Inference

Dustin Tran, Rajesh Ranganath, David M. Blei

Type: Preprint

Publication Date: 2017-02-28

Citations: 90

Locations

arXiv (Cornell University) - View

Similar Works

Action	Title	Year	Authors
+	Hierarchical Implicit Models and Likelihood-Free Variational Inference	2017	Dustin Tran Rajesh Ranganath David M. Blei
+	Adversarial Variational Optimization of Non-Differentiable Simulators	2017	Gilles Louppe Joeri Hermans K. Cranmer
+	Adversarial Variational Optimization of Non-Differentiable Simulators	2017	Gilles Louppe Joeri Hermans K. Cranmer
+	Hierarchical Variational Models	2015	Rajesh Ranganath Dustin Tran David M. Blei
+	Hierarchical Variational Models	2015	Rajesh Ranganath Dustin Tran David M. Blei
+	Asymptotically exact conditional inference in deep generative models and differentiable simulators	2016	Matthew M. Graham Amos Storkey
+ PDF Chat	Variational Autoencoders for Efficient Simulation-Based Inference	2024	Mayank Nautiyal Andrey Shternshis Andreas Hellander Prashant Singh
+	Automatic Differentiation Variational Inference	2016	Alp Kucukelbir Dustin Tran Rajesh Ranganath Andrew Gelman David M. Blei
+	Automatic Differentiation Variational Inference	2016	Alp Kucukelbir Dustin Tran Rajesh Ranganath Andrew Gelman David M. Blei
+	Abstraction of Markov Population Dynamics via Generative Adversarial Nets	2021	Francesca Cairoli Ginevra Carbone Luca Bortolussi
+	Abstraction of Markov Population Dynamics via Generative Adversarial Nets	2021	Francesca Cairoli Ginevra Carbone Luca Bortolussi
+	Asymptotically exact inference in likelihood-free models	2016	Matthew M. Graham Amos Storkey
+	Two Methods For Wild Variational Inference	2016	Qiang Liu Yihao Feng
+	Variational methods for simulation-based inference	2022	Manuel Glöckler Michael Deistler Jakob H. Macke
+ PDF Chat	Predictive variational inference: Learn the predictively optimal posterior distribution	2024	Jinlin Lai Yuling Yao
+	Generative Flow Networks for Discrete Probabilistic Modeling	2022	Dinghuai Zhang Nikolay Malkin Zhen Liu Alexandra Volokhova Aaron Courville Yoshua Bengio
+	Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders	2020	Benoit Gaujac Ilya Feige David Barber
+ PDF Chat	Generative Bayesian Modeling with Implicit Priors	2024	Luna Fazio Maximilian Scholz Paul‐Christian Bürkner
+	A Theory of Independent Mechanisms for Extrapolation in Generative Models	2020	Michel Besserve Rémy Sun Dominik Janzing Bernhard Schölkopf
+	A theory of independent mechanisms for extrapolation in generative models	2020	Michel Besserve Rémy Sun Dominik Janzing Bernhard Schölkopf

Works That Cite This (73)

Action	Title	Year	Authors
+ PDF Chat	OpenGAN: Open Set Generative Adversarial Networks	2021	Luke Ditria Benjamin Meyer Tom Drummond
+ PDF Chat	MPCC: Matching Priors and Conditionals for Clustering	2020	Nicolás Astorga P. Huijse Pavlos Protopapas P. A. Estévez
+	Discriminator Rejection Sampling	2018	Samaneh Azadi Catherine Olsson Trevor Darrell Ian Goodfellow Augustus Odena
+	Wasserstein Variational Inference	2018	Luca Ambrogioni Umut Güçlü Yağmur Güçlütürk Max Hinne Eric Maris Marcel van Gerven
+	Consistency Regularization for Generative Adversarial Networks.	2019	Han Zhang Zizhao Zhang Augustus Odena Honglak Lee
+	A Contemporary Overview of Probabilistic Latent Variable Models	2017	Rick Farouni
+	Capturing the diversity of biological tuning curves using generative adversarial networks.	2017	Takafumi Arakaki G. Barello Yashar Ahmadian
+	MadMiner: Machine learning-based inference for particle physics	2019	Johann Brehmer Felix Kling Irina Espejo K. Cranmer
+	Likelihood-free inference with an improved cross-entropy estimator	2018	Markus Stoye Johann Brehmer Gilles Louppe Juan Pavez K. Cranmer
+	Self-Attention Generative Adversarial Networks	2018	Han Zhang Ian Goodfellow Dimitris Metaxas Augustus Odena

Works Cited by This (33)

Action	Title	Year	Authors
+	Monte Carlo Methods of Inference for Implicit Statistical Models	1984	Peter J. Diggle Richard J. Gratton
+	Learning Stochastic Recurrent Networks	2014	Justin Bayer Christian Osendorfer
+	Markov Chain Monte Carlo and Variational Inference: Bridging the Gap	2014	Tim Salimans Diederik P. Kingma Max Welling
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra
+	The Probability of Heads	1986	Joseph B. Keller
+	Statistical inference for stochastic simulation models - theory and application	2011	Florian Härtig Justin M. Calabrese Björn Reineking Thorsten Wiegand Andreas Huth
+ PDF Chat	Approximate Bayesian computational methods	2011	Jean‐Michel Marin Pierre Pudlo Christian P. Robert Robin Ryder
+	Copula variational inference	2015	Dustin Tran David M. Blei Edoardo M. Airoldi
+	Stochastic variational inference	2013	Matthew D. Hoffman David M. Blei Chong Wang John Paisley
+	Approximate Bayesian Computation in Evolution and Ecology	2010	Mark Beaumont