这篇论文提出Action-BED，把贝叶斯实验设计的目标从双重难解变成单重难解，直接用随机梯度优化，更简单高效。
传统贝叶斯实验设计(BED)基于最大化预期不确定性减少，导致双重难解目标难以优化。该论文提出Action-BED，基于预期未来损失(EFL)的任务驱动框架，将目标简化为单重难解问题。通过随机梯度联合优化设计策略和动作策略，无需显式后验或边际似然估计。只需要从联合模型采样并评估下游损失函数，比现有方法更有效、高效、简单。
Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives
Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfall of this approach is that it leads to doubly intractable objectives that are difficult to optimise, while customising them to particular downstream tasks of interest can also be difficult. Following first principles decision theory, we demonstrate that BED can alternatively be formulated in terms of an expected future loss (EFL) on downstream actions, providing a simple and naturally task-driven framework. Critically, we then show that all such EFLs can be rearranged into singly intractable objectives that can be jointly optimised with respect to both the design policy and a downstream action policy using stochastic gradients, an approach we refer to as ACTION-BED. This formulation further sidesteps the need for any explicit posterior or marginal likelihood estimation and is naturally implicit, requiring only the ability to sample from the joint model over model parameters and data, and evaluate the downstream loss function. It thus allows design policies to be learned more effectively, efficiently, and simply than existing methods, while providing easy customisation to different downstream tasks and losses.