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

Desi R Ivanova, Adam Foster, Steven Kleinegesse, Michael U Gutmann, Tom Rainforth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeuRIPS 2021)
PublisherNeural Information Processing Systems
Number of pages33
Publication statusAccepted/In press - 28 Sep 2021
Event35th Conference on Neural Information Processing Systems - Virtual
Duration: 6 Dec 202114 Dec 2021
https://nips.cc/

Conference

Conference35th Conference on Neural Information Processing Systems
Abbreviated titleNeurIPS 2021
Period6/12/2114/12/21
Internet address

Fingerprint

Dive into the research topics of 'Implicit Deep Adaptive Design: Policy–Based Experimental Design without Likelihoods'. Together they form a unique fingerprint.

Cite this