通过问答式通信实现高效序列假设检验，降低实时访问成本。
The Query/Hit Model for Sequential Hypothesis Testing
- 用查询与响应等待时间构建通信机制，避免直接访问源数据。
- 提出动态侦察-哨兵算法，显著降低错误概率，提升检测速度。
- 适用于隐私受限、计算资源紧张的用户行为分析场景。
本文提出查询/命中（Query/Hit, Q/H）学习模型，由两名代理构成：Alice可访问流式数据源，而Bob无法直接访问。通信通过一系列查询-命中对进行：Bob发送源符号序列（查询），Alice返回每个查询在数据流中出现前的等待时间（命中）。该模型适用于通信、计算和隐私约束下无法实时访问源数据的场景。本文刻画了在Q/H模型下的序列假设检验误差指数，并提出动态侦察-哨兵算法（DSSA），利用互信息神经估计器评估每条查询的误差指数，选择效率最高的查询。在合成数据及真实数据集（包括鼠标轨迹、排版模式、触控用户交互）上进行了广泛实验，结果表明所提策略在错误概率、查询选择和检测时间方面均优于基线方法。
原文摘要 · Abstract (English)
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.
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