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Gradient estimation -- approximating the gradient of an expectation with respect to the parameters of a distribution -- is central to the solution of many machine learning problems.
The classification of birth and death processes
Samuel Karlin and James McGregor · 1957
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Monte Carlo calculations of the radial distribution functions for a proton? electron plasma
Av A Barker · 1965
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A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Charles Stein · 1972
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Stein’s method and Poisson process convergence
A. D. Barbour · 1988
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Likelihood ratio gradient estimation for stochastic systems
Peter W. Glynn · 1990
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Variance reduction via an approximating Markov process
Shane G Henderson · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Zero-variance principle for Monte Carlo algorithms
Roland Assaraf and Michel Caffarel · 1999
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Stein’s Method and Birth-Death Processes
Timothy C. Brown and Aihua Xia · 2001
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The optimal reward baseline for gradient-based reinforcement learning
Lex Weaver and Nigel Tao · 2001
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Stein’s method for birth and death chains
Susan Holmes · 2004
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Use of exchangeable pairs in the analysis of simulations
Charles Stein, Persi Diaconis, Susan Holmes, and Gesine Reinert · 2004
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Stochastic processes and models
David Stirzaker · 2005
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Stein’s method for discrete Gibbs measures
Peter Eichelsbacher and Gesine Reinert · 2008
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Introduction to Markov chain Monte Carlo
Charles J. Geyer · 2011
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Minimum probability flow learning
Jascha Sohl-Dickstein, Peter Battaglino, and Michael R DeWeese · 2011
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Control variates for estimation based on reversible Markov chain Monte Carlo samplers
Petros Dellaportas and Ioannis Kontoyiannis · 2012
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Variational Bayesian inference with stochastic search
John Paisley, David M Blei, and Michael I Jordan · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Zero variance Markov chain Monte Carlo for Bayesian estimators
Antonietta Mira, Reza Solgi, and Daniele Imparato · 2013
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Monte Carlo theory, methods and examples
Art B. Owen · 2013
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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor · 2014
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Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David Blei · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo J. Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Tim Salimans and David A Knowles · 2014
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Doubly stochastic variational Bayes for non-conjugate inference
Michalis K. Titsias and Miguel Lázaro-Gredilla · 2014
Cited alongside, same era.
Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
Cited alongside, same era.
Measuring sample quality with Stein’s method
Jackson Gorham and Lester Mackey · 2015
Goodness-of-fit testing for discrete distributions via Stein discrepancy
Jiasen Yang, Qiang Liu, Vinayak Rao, and Jennifer Neville · 2018
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Minimum Stein discrepancy estimators
Alessandro Barp, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, and Lester Mackey · 2019
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Stein’s method for stationary distributions of Markov chains and application to Ising models
Guy Bresler and Dheeraj Nagaraj · 2019
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GO gradient for expectation-based objectives
Yulai Cong, Miaoyun Zhao, Ke Bai, and Lawrence Carin · 2019
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Buy 4 REINFORCE samples, get a baseline for free!
W. Kool, H. V. Hoof, and M. Welling · 2019
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Approximating stationary distributions of fast mixing Glauber dynamics, with applications to exponential random graphs
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Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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Gradient estimation using stochastic computation graphs
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel · 2015
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Local expectation gradients for black box variational inference
Michalis K Titsias and Miguel Lázaro-Gredilla · 2015
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MuProp: Unbiased backpropagation for stochastic neural networks
Shixiang Gu, Sergey Levine, Ilya Sutskever, and Andriy Mnih · 2016
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Variational inference for Monte Carlo objectives
Andriy Mnih and Danilo Rezende · 2016
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Categorical reparameterization with Gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Gesine Reinert and Nathan Ross · 2019
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ARM: Augment-REINFORCE-merge gradient for stochastic binary networks
Mingzhang Yin and Mingyuan Zhou · 2019
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DisARM: An antithetic gradient estimator for binary latent variables
Zhe Dong, Andriy Mnih, and George Tucker · 2020
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The reproducing stein kernel approach for post-hoc corrected sampling
Liam Hodgkinson, Robert Salomone, and Fred Roosta · 2020
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Estimating gradients for discrete random variables by sampling without replacement
Wouter Kool, Herke van Hoof, and Max Welling · 2020
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Monte Carlo gradient estimation in machine learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, and Andriy Mnih · 2020
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VarGrad: A low-variance gradient estimator for variational inference
Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco Ruiz, and Omer Deniz Akyildiz · 2020
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Scalable control variates for Monte Carlo methods via stochastic optimization
Shijing Si, Chris Oates, Andrew B Duncan, Lawrence Carin, and François-Xavier Briol · 2020
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NVAE: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
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Probabilistic best subset selection via gradient-based optimization, 2020
Mingzhang Yin, Nhat Ho, Bowei Yan, Xiaoning Qian, and Mingyuan Zhou · 2020
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Informed proposals for local MCMC in discrete spaces
Giacomo Zanella · 2020
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Stein’s method meets statistics: A review of some recent developments
Andreas Anastasiou, Alessandro Barp, François-Xavier Briol, Bruno Ebner, Robert E Gaunt, Fatemeh Ghaderinezhad, Jackson Gorham, Arthur Gretton, Christophe Ley, Qiang Liu, and Lester Mackey · 2021
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ARMS: Antithetic-REINFORCE-Multi-Sample gradient for binary variables
Aleksandar Dimitriev and Mingyuan Zhou · 2021
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Coupled gradient estimators for discrete latent variables
Zhe Dong, Andriy Mnih, and George Tucker · 2021
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Oops I took a gradient: Scalable sampling for discrete distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, and Chris Maddison · 2021
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Implicit MLE: backpropagating through discrete exponential family distributions
Mathias Niepert, Pasquale Minervini, and Luca Franceschi · 2021
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A unified view of likelihood ratio and reparameterization gradients
Paavo Parmas and Masashi Sugiyama · 2021
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Double control variates for gradient estimation in discrete latent variable models
Michalis K Titsias and Jiaxin Shi · 2022
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