通过消除法优化主动假设检验，显著缩短判断时间。
Finite-Sample Analysis of Elimination in Active Hypothesis Testing
- 设计可动态剪枝假设集的跟踪停止算法，提升检测效率。
- 理论证明期望停止时间在有限样本下有更紧上界。
- 适合对可靠性与速度兼备的高安全场景应用。
在诸多高安全要求的应用中，固定置信度的有限样本主动假设检验问题至关重要。本文在序贯假设检验框架下，研究假设消除对停止时间的影响。提出一种增强型追踪-停止算法，通过逐步剔除非最优假设并重新分配感知资源，聚焦于剩余候选。理论分析给出了期望停止时间的非渐近上界，消除带来的性能增益体现在非主导项，源于在缩小后的假设集上更紧的跟踪与集中常数。此外，引入激进性参数以调节快速消除与弱置信度之间的权衡。在合成高斯实例上的实验验证了理论预测。
原文摘要 · Abstract (English)
A fixed-confidence, finite-sample problem of active hypothesis testing arises in many safety-critical applications. Situated in the context of sequential hypothesis testing, this paper studies the effect of hypothesis elimination on the stopping time. We introduce an elimination-augmented Track-and-Stop algorithm, in which champion-specific active-opponent sets are progressively pruned, and sensing effort is reallocated toward the surviving alternatives. Our analysis derives a non-asymptotic upper bound on the expected stopping time. The gain in finite-sample from elimination appears on the scale of the non-leading term, resulting from tighter tracking and concentration constants on the reduced hypothesis set. Furthermore, we introduce an aggressiveness parameter to modulate the trade-off between faster elimination and weaker confidence guarantee. An experimental study on synthetic Gaussian instances confirms the theoretical predictions.
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。