Fetching the paper…
Reading the bibliography…
Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior.
Correcting Predictions for Approximate Bayesian Inference
Tomasz Kuśmierczyk, Joseph Sakaya, and Arto Klami · 1902
Earlier work this paper cites.
Statistical Decision Theory and Bayesian Analysis; 2nd edition
James O Berger · 1985
Earlier work this paper cites.
Expectation Propagation for Approximate Bayesian Inference
Thomas P. Minka · 2001
Earlier work this paper cites.
The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation
Christian Robert · 2007
Earlier work this paper cites.
A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation
Yee W Teh, David Newman, and Max Welling · 2007
Earlier work this paper cites.
Probabilistic Matrix Factorization
Andriy Mnih and Ruslan Salakhutdinov · 2008
Earlier work this paper cites.
Approximate inference for the loss-calibrated Bayesian
Simon Lacoste-Julien, Ferenc Huszár, and Zoubin Ghahramani · 2011
Earlier work this paper cites.
The Million Song Dataset
Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere · 2011
Earlier work this paper cites.
Bayesian Data Analysis
Andrew Gelman, Hal S Stern, John B Carlin, David B Dunson, Aki Vehtari, and Donald B Rubin · 2013
Earlier work this paper cites.
Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients
Tom Schaul and Yann LeCun · 2013
Earlier work this paper cites.
Black Box Variational Inference
Rajesh Ranganath, Sean Gerrish, and David Blei · 2014
Cited alongside, same era.
Doubly Stochastic Variational Bayes for non-Conjugate Inference
Michalis Titsias and Miguel Lázaro-Gredilla · 2014
Cited alongside, same era.
Loss-calibrated Monte Carlo Action Selection
Ehsan Abbasnejad, Justin Domke, and Scott Sanner · 2015
Cited alongside, same era.
Stochastic Structured Variational Inference
Matthew Hoffman and David Blei · 2015
Cited alongside, same era.
Variational Inference with Normalizing Flow
Danilo Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Christian Naesseth, Francisco Ruiz, Scott Linderman, and David Blei · 2017
Later among the works it cites.
Boosting Variational Inference
Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, and David B Dunson · 2017
Later among the works it cites.
Loss-Calibrated Approximate Inference in Bayesian Neural Networks
Adam D Cobb, Stephen J Roberts, and Yarin Gal · 2018
Later among the works it cites.
Yes, but did it work?: Evaluating variational inference
Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman · 2018
Later among the works it cites.
Implicit Reparameterization Gradients
Mikhail Figurnov, Shakir Mohamed, and Andriy Mnih · 2018
Later among the works it cites.
Boosting Black Box Variational Inference
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Francisco J.R. Ruiz, Michalis Titsias, and David Blei · 2016
Cited alongside, same era.
Variational Inference: A Review for Statisticians
David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe · 2017
Cited alongside, same era.
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
Andrew Gelman, Aki Vehtari, Pasi Jylänki, Tuomas Sivula, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P Cunningham, David Schiminovich, and Christian Robert · 2017
Cited alongside, same era.
Stan: A Probabilistic Programming Language
Bob Carpenter, Andrew Gelman, Matthew D Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell · 2017
Cited alongside, same era.
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, and Gunnar Raetsch · 2018
Later among the works it cites.
Variational inference and model selection with generalized evidence bounds
Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, and Lawrence Carin Duke · 2018
Later among the works it cites.
Tighter variational bounds are not necessarily better
Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, and Yee Whye Teh · 2018
Later among the works it cites.
Generalized Variational Inference
Jeremias Knoblauch, Jack Jewson, and Theodoros Damoulas · 2019
Closest in time.