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We introduce implicit Deep Adaptive Design (iDAD), a new method forperforming adaptive experiments in real-time with implicit models. iDADamortizes the cost of Bayesian optimal experimental design (BOED) by learning adesign policy network upfront, which can then be deployed quickly at the timeof the experiment. The iDAD network can be trained on any model which simulatesdifferentiable samples, unlike previous design policy work that requires aclosed form likelihood and conditionally independent experiments. Atdeployment, iDAD allows design decisions to be made in milliseconds, incontrast to traditional BOED approaches that require heavy computation duringthe experiment itself. We illustrate the applicability of iDAD on a number ofexperiments, and show that it provides a fast and effective mechanism forperforming adaptive design with implicit models.

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods