  We introduce Deep Adaptive Design (DAD), a general method for amortizing the
cost of performing sequential adaptive experiments using the framework of
Bayesian optimal experimental design (BOED). Traditional sequential BOED
approaches require substantial computational time at each stage of the
experiment. This makes them unsuitable for most real-world applications, where
decisions must typically be made quickly. DAD addresses this restriction by
learning an amortized design network upfront and then using this to rapidly run
(multiple) adaptive experiments at deployment time. This network takes as input
the data from previous steps, and outputs the next design using a single
forward pass; these design decisions can be made in milliseconds during the
live experiment. To train the network, we introduce contrastive information
bounds that are suitable objectives for the sequential setting, and propose a
customized network architecture that exploits key symmetries. We demonstrate
that DAD successfully amortizes the process of experimental design,
outperforming alternative strategies on a number of problems.
