Publications

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

iDAD allows us to practically run Bayesian optimal experiments with implicit (likelihood-free) models in real-time. Previous methods either relied on an explicit likelihood model of the outcomes, or were too computationally costly to run in real-time.

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

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

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

DAD is the first policy-based approach to BOED that enables adaptive experiments to be performed in real-time.

Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design