让预测模型学会选最优的前K个干预地点，提升资源利用效率。
Decision-aware training of spatiotemporal forecasting models to select a top K subset of sites for intervention
- 基于决策理论设计更优的站点排序方法，优于简单平均值
- 提出新训练目标，使模型在选前K点时表现接近最优解
- 适用于公共卫生与生态保护等需精准选址的场景
资源有限时，决策者常需从众多地点中选出最多K个进行干预。时空预测模型可实现数据驱动的决策。近期提出的‘最佳可达比例’（BPR）衡量使用模型推荐的前K个地点相比事后最优解的性能损失。本文解决两个开放问题：首先，针对联合预测各站点事件数的概率模型，提出一种优于单站均值的决策导向排序方法；其次，针对离散选点导致梯度为零的训练难题，采用扰动优化器突破瓶颈，并设计结合似然与决策感知BPR约束的训练目标，既保证整体预测精度，又提升前K名选址质量。在缓解阿片类药物致死过量和监测濒危野生动物两个实际场景中验证了方法的有效性。
原文摘要 · Abstract (English)
Optimal allocation of scarce resources is a common problem for decision makers faced with choosing a limited number of locations for intervention. Spatiotemporal prediction models could make such decisions data-driven. A recent performance metric called fraction of best possible reach (BPR) measures the impact of using a model's recommended size K subset of sites compared to the best possible top-K in hindsight. We tackle two open problems related to BPR. First, we explore how to rank all sites numerically given a probabilistic model that predicts event counts jointly across sites. Ranking via the per-site mean is suboptimal for BPR. Instead, we offer a better ranking for BPR backed by decision theory. Second, we explore how to train a probabilistic model's parameters to maximize BPR. Discrete selection of K sites implies all-zero parameter gradients which prevent standard gradient training. We overcome this barrier via advances in perturbed optimizers. We further suggest a training objective that combines likelihood with a decision-aware BPR constraint to deliver high-quality top-K rankings as well as good forecasts for all sites. We demonstrate our approach on two where-to-intervene applications: mitigating opioid-related fatal overdoses for public health and monitoring endangered wildlife.
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