Amortized Bayesian Experimental Design for Decision-Making
| Title: | Amortized Bayesian Experimental Design for Decision-Making |
|---|---|
| Authors: | Huang, Daolang, Guo, Yujia, Acerbi, Luigi, Kaski, Samuel |
| Publication Year: | 2024 |
| Collection: | Computer Science Statistics |
| Subject Terms: | Statistics - Machine Learning, Computer Science - Machine Learning |
| More Details: | 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. Comment: 20 pages, 6 figures. Accepted at the 38th Conference on Neural Information Processing Systems (NeurIPS 2024) |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2411.02064 |
| Accession Number: | edsarx.2411.02064 |
| Database: | arXiv |
| Description not available. |