标题： 查询/命中模型用于顺序假设检验 显示英文标题

标题： The Query/Hit Model for Sequential Hypothesis Testing

摘要： 本工作介绍了查询/命中（Q/H）学习模型。 设置包括两个代理。 一个代理，爱丽丝，可以访问流式源，而另一个代理，鲍勃，没有直接访问源的权限。 通信通过顺序的Q/H对进行：鲍勃发送一系列源符号（查询），爱丽丝则以每个查询在源流中出现的等待时间（命中）作为响应。 该模型受到通信、计算和隐私约束的启发，这些约束限制了对源的实时访问。 对Q/H模型下顺序假设检验的误差指数进行了表征，并提出了一种查询策略，即动态侦察-哨兵算法（DSSA）。 该策略使用互信息神经估计器来计算与每个查询相关的误差指数，并选择效率最高的查询。 提供了在合成和现实世界数据集上的广泛实验评估——包括鼠标移动轨迹、排版模式和基于触摸的用户交互——以比较所提出的策略与基线在错误概率、查询选择和检测时间方面的性能。 显示英文摘要

摘要： This work introduces the Query/Hit (Q/H) learning model. The setup consists of two agents. One agent, Alice, has access to a streaming source, while the other, Bob, does not have direct access to the source. Communication occurs through sequential Q/H pairs: Bob sends a sequence of source symbols (queries), and Alice responds with the waiting time until each query appears in the source stream (hits). This model is motivated by scenarios with communication, computation, and privacy constraints that limit real-time access to the source. The error exponent for sequential hypothesis testing under the Q/H model is characterized, and a querying strategy, the Dynamic Scout-Sentinel Algorithm (DSSA), is proposed. The strategy employs a mutual information neural estimator to compute the error exponent associated with each query and to select the query with the highest efficiency. Extensive empirical evaluations on both synthetic and real-world datasets -- including mouse movement trajectories, typesetting patterns, and touch-based user interactions -- are provided to evaluate the performance of the proposed strategy in comparison with baselines, in terms of probability of error, query choice, and time-to-detection.