让实验设计直接服务于决策，提升医疗和定价等关键场景的准确性。
Amortized Bayesian Experimental Design for Decision-Making
- 用Transformer架构统一设计实验与决策，实时生成最优实验方案。
- 相比传统方法，实验数据更利于后续决策，显著提升决策准确率。
- 适合需要快速迭代实验与决策的场景，如个性化医疗和动态定价。
许多关键决策，如个性化医疗诊断和产品定价，依赖于一系列实验的设计、观测与分析所得洞察。这凸显了实验设计的重要性——它不仅关乎系统参数信息的收集（如传统贝叶斯实验设计），更直接影响下游决策。现有大多数贝叶斯实验设计方法采用摊销策略网络实现快速实验设计，但所获信息对后续决策支持不足，因实验未针对决策目标优化。本文提出一种摊销式决策感知的贝叶斯实验设计框架，优先最大化下游决策效用。我们引入新型架构Transformer神经决策过程（TNDP），可即时提出下一阶段实验设计，并同步推断下游决策，从而在统一流程中摊销两项任务。我们在多个任务上验证了该方法的有效性，结果表明其能生成高信息量实验设计，显著提升决策准确性。
原文摘要 · Abstract (English)
Many critical decisions, such as personalized medical diagnoses and product pricing, are made based on insights gained from designing, observing, and analyzing a series of experiments. This highlights the crucial role of experimental design, which goes beyond merely collecting information on system parameters as in traditional Bayesian experimental design (BED), but also plays a key part in facilitating downstream decision-making. Most recent BED methods use an amortized policy network to rapidly design experiments. However, the information gathered through these methods is suboptimal for down-the-line decision-making, as the experiments are not inherently designed with downstream objectives in mind. In this paper, we present an amortized decision-aware BED framework that prioritizes maximizing downstream decision utility. We introduce a novel architecture, the Transformer Neural Decision Process (TNDP), capable of instantly proposing the next experimental design, whilst inferring the downstream decision, thus effectively amortizing both tasks within a unified workflow. We demonstrate the performance of our method across several tasks, showing that it can deliver informative designs and facilitate accurate decision-making.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。