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Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
Desi R Ivanova · Adam Foster · Steven Kleinegesse · Michael Gutmann · Thomas Rainforth

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @

We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian optimal experimental design (BOED) by learning a design policy network upfront, which can then be deployed quickly at the time of the experiment. The iDAD network can be trained on any model which simulates differentiable samples, unlike previous design policy work that requires a closed form likelihood and conditionally independent experiments. At deployment, iDAD allows design decisions to be made in milliseconds, in contrast to traditional BOED approaches that require heavy computation during the experiment itself. We illustrate the applicability of iDAD on a number of experiments, and show that it provides a fast and effective mechanism for performing adaptive design with implicit models.

Author Information

Desi R Ivanova (University of Oxford)
Adam Foster (Microsoft Research Cambridge)
Steven Kleinegesse (The University of Edinburgh)
Michael Gutmann (University of Edinburgh)
Thomas Rainforth (University of Oxford)

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