通过设定行动截止时间，动态控制随机成本下的检测开销。
On Cost-Aware Sequential Hypothesis Testing with Random Costs and Action Cancellation
- 引入可调截止时间，允许中止进行中的高成本动作
- 在事前成本模型下，截止时间可降低总期望成本至恒定成本水平
- 适用于成本随机且需权衡误差与开销的在线决策场景
我们研究了一种成本感知的序贯假设检验变体：单个决策者（DM）选择具有正随机成本的动作，在平均误差约束下识别真假设，同时最小化期望总成本。决策者可中止正在进行的动作，放弃采样，将实际成本截断至一个可调节的确定性上限，称为每动作截止时间。分析了两种成本揭示模型下的策略：事后揭示（成本仅在获得样本后揭示）和事前揭示（成本在采样前累积）。在事后模型中，截止时间不影响期望总成本，成本-误差权衡等同于用成本均值替换确定成本的基准情况。在事前模型中，我们发现截止时间会增加动作执行次数的期望值，但通过引入有效每动作成本，可使期望总成本降至恒定成本情形。我们刻画了截止时间何时有益，并详细研究了几类具体情形。
原文摘要 · Abstract (English)
We study a variant of cost-aware sequential hypothesis testing in which a single active Decision Maker (DM) selects actions with positive, random costs to identify the true hypothesis under an average error constraint, while minimizing the expected total cost. The DM may abort an in-progress action, yielding no sample, by truncating its realized cost at a smaller, tunable deterministic limit, which we term a per-action deadline. We analyze how this cancellation option can be exploited under two cost-revelation models: ex-post, where the cost is revealed only after the sample is obtained, and ex-ante, where the cost accrues before sample acquisition. In the ex-post model, per-action deadlines do not affect the expected total cost, and the cost-error tradeoffs coincide with the baseline obtained by replacing deterministic costs with cost means. In the ex-ante model, we show how per-action deadlines inflate the expected number of times actions are applied, and that the resulting expected total cost can be reduced to the constant-cost setting by introducing an effective per-action cost. We characterize when deadlines are beneficial and study several families in detail.
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。