Fetching the paper…
Reading the bibliography…
We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples.
Completeness, similar regions, and unbiased estimation: Part i
EL Lehmann and Henry Scheffé · 1950
Earlier work this paper cites.
Some estimators in sampling with varying probabilities without replacement
Des Raj · 1956
Earlier work this paper cites.
Ordered and unordered estimators in sampling without replacement
MN Murthy · 1957
Earlier work this paper cites.
Individual choice behavior: A theoretical analysis
R Duncan Luce · 1959
Earlier work this paper cites.
The analysis of permutations
Robin L Plackett · 1975
Earlier work this paper cites.
The relationship between Luce’s choice axiom, Thurstone’s theory of comparative judgment, and the double exponential distribution
John I Yellott · 1977
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Rao-Blackwellisation of sampling schemes
George Casella and Christian P Robert · 1996
Earlier work this paper cites.
On-line inference for hidden markov models via particle filters
Paul Fearnhead and Peter Clifford · 2003
Earlier work this paper cites.
Comparison of resampling schemes for particle filtering
Randal Douc and Olivier Cappé · 2005
Earlier work this paper cites.
Priority sampling for estimation of arbitrary subset sums
Nick Duffield, Carsten Lund, and Mikkel Thorup · 2007
Earlier work this paper cites.
On the quantitative analysis of deep belief networks
Ruslan Salakhutdinov and Iain Murray · 2008
Earlier work this paper cites.
The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
Earlier work this paper cites.
Variational Bayesian inference with stochastic search
John Paisley, David M Blei, and Michael I Jordan · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Earlier work this paper cites.
Deep autoregressive networks
Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, and Daan Wierstra · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
A* sampling
Chris J Maddison, Daniel Tarlow, and Tom Minka · 2014
Earlier work this paper cites.
Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor · 2014
Cited alongside, same era.
Black box variational inference
Rajesh Ranganath, Sean Gerrish, and David Blei · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Cited alongside, same era.
Gumbel-max trick and weighted reservoir sampling, 2014
Tim Vieira · 2014
Cited alongside, same era.
Arsm: Augment-reinforce-swap-merge estimator for gradient backpropagation through categorical variables
Mingzhang Yin, Yuguang Yue, and Mingyuan Zhou · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Minimum risk training for neural machine translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu · 2016
Later among the works it cites.
An actor-critic algorithm for sequence prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio · 2017
Later among the works it cites.
Self-critical sequence training for image captioning
Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel · 2017
Later among the works it cites.
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Geoffrey Roeder, Yuhuai Wu, and David K Duvenaud · 2017
Later among the works it cites.
Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, and Jascha Sohl-Dickstein · 2017
Later among the works it cites.
Estimating means in a finite universe, 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gradient estimation using stochastic computation graphs
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel · 2015
Cited alongside, same era.
Local expectation gradients for black box variational inference
Michalis K Titsias and Miguel Lázaro-Gredilla · 2015
Cited alongside, same era.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
Cited alongside, same era.
Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
Cited alongside, same era.
Muprop: Unbiased backpropagation for stochastic neural networks
Shixiang Gu, Sergey Levine, Ilya Sutskever, and Andriy Mnih · 2016
Cited alongside, same era.
Dual learning for machine translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma · 2016
Cited alongside, same era.
Tim Vieira · 2017
Later among the works it cites.
Classical structured prediction losses for sequence to sequence learning
Sergey Edunov, Myle Ott, Michael Auli, David Grangier, et al · 2018
Later among the works it cites.
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, and David Duvenaud · 2018
Later among the works it cites.
Neural machine translation with Gumbel-greedy decoding
Jiatao Gu, Daniel Jiwoong Im, and Victor OK Li · 2018
Later among the works it cites.
Searnn: Training RNNs with global-local losses
Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, and Simon Lacoste-Julien · 2018
Later among the works it cites.
Memory augmented policy optimization for program synthesis and semantic parsing
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V Le, and Ni Lao · 2018
Later among the works it cites.
Direct optimization through argmax for discrete variational auto-encoder
Guy Lorberbom, Andreea Gane, Tommi Jaakkola, and Tamir Hazan · 2018
Later among the works it cites.
Learning beam search policies via imitation learning
Renato Negrinho, Matthew Gormley, and Geoffrey J Gordon · 2018
Later among the works it cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Later among the works it cites.
Stochastic optimization of sorting networks via continuous relaxations
Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon · 2019
Later among the works it cites.
Rao-Blackwellized stochastic gradients for discrete distributions
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael Jordan, and Jon Mcauliffe · 2019
Later among the works it cites.
Direct policy gradients: Direct optimization of policies in discrete action spaces
Guy Lorberbom, Chris J Maddison, Nicolas Heess, Tamir Hazan, and Daniel Tarlow · 2019
Later among the works it cites.