ACTION-BED

ACTION-BED: Task-driven Bayesian experimental design via singly intractable expected future loss objectives

A new arXiv preprint proposes ACTION-BED, a reformulation of Bayesian experimental design (BED) that replaces the traditional doubly intractable expected information gain objective with an expected future loss (EFL) on downstream actions. The authors show all such EFLs can be rearranged into singly intractable objectives jointly optimizable over design and action policies via stochastic gradients, eliminating the need for explicit posterior or marginal likelihood estimation. The method is claimed to be more efficient and customizable to downstream tasks than existing BED approaches.