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.

提出了一种利用隐式模型进行的实时自适应实验的新方法 - iDAD，通过学习设计策略网络来摊销贝叶斯最优实验设计的成本，并可在实验时快速部署，可以在毫秒内做出设计决策。