In many areas of medicine, security, and life sciences, we want to allocate limited resources to different sources in order to detect extreme values. In this paper, we study an efficient way to allocate these resources sequentially under limited feedback. While sequential design of experiments is well studied in bandit theory, the most commonly optimized property is the regret with respect to the maximum mean reward. However, in other problems such as network intrusion detection, we are interested in detecting the most extreme value output by the sources. Therefore, in our work we study extreme regret which measures the efficiency of an algorithm compared to the oracle policy selecting the source with the heaviest tail. We propose the ExtremeHunter algorithm, provide its analysis, and evaluate it empirically on synthetic and real-world experiments.

翻译：在医学、安全及生命科学等多个领域，我们常需将有限资源分配给不同来源，以检测极值。本文研究在有限反馈条件下，如何高效地序贯分配这些资源。尽管赌博机理论已充分研究序贯实验设计，但最常优化的性能指标是相对于最大均值回报的遗憾值。然而，在诸如网络入侵检测等其它问题中，我们关注的是检测各来源输出的最极端值。因此，本文研究极值遗憾——该指标衡量算法与选择具有最重尾分布来源的完美策略相比的效率。我们提出ExtremeHunter算法，给出其理论分析，并在合成数据与真实数据实验上对其进行实证评估。