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We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED).
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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On a measure of the information provided by an experiment
Dennis V Lindley · 1956
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Capital-labor substitution and economic efficiency
Kenneth J Arrow, Hollis B Chenery, Bagicha S Minhas, and Robert M Solow · 1961
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Asymptotic evaluation of certain Markov process expectations for large time
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Bayesian experimental design: A review
Kathryn Chaloner and Isabella Verdinelli · 1995
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The IM algorithm: a variational approach to information maximization
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Optimal observation times in experimental epidemic processes
Alex R Cook, Gavin J Gibson, and Christopher A Gilligan · 2008
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger · 2009
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Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington, and Frank Wood · 2018
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Doubly reparameterized gradient estimators for monte carlo objectives
George Tucker, Dieterich Lawson, Shixiang Gu, and Chris J Maddison · 2018
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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A robust approach to sequential information theoretic planning
Sue Zheng, Jason Pacheco, and John Fisher · 2018
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Variational Bayesian Optimal Experimental Design
Adam Foster, Martin Jankowiak, Elias Bingham, Paul Horsfall, Yee Whye Teh, Thomas Rainforth, and Noah Goodman · 2019
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