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Spotlight
Tue Dec 10 04:40 PM -- 04:45 PM (PST) @ West Ballroom C
Variational Bayesian Optimal Experimental Design
Adam Foster · Martin Jankowiak · Eli Bingham · Paul Horsfall · Yee Whye Teh · Tom Rainforth · Noah Goodman
[ Paper [ Poster [ Slides

Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several classes of fast EIG estimators by building on ideas from amortized variational inference. We show theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches. We further demonstrate the practicality of our approach on a number of end-to-end experiments.