针对极端值检测难题，提出高效资源分配算法ExtremeHunter。
Extreme bandits
- 基于极端尾部概率设计新型序列实验策略。
- 在合成与真实数据上显著优于传统方法。
- 适合网络安全、医学等极端事件监测场景。
在医疗、安全和生命科学等领域，需在有限资源下分配给不同来源以检测极端值。本文研究在反馈受限条件下高效进行资源的顺序分配。尽管序贯实验设计在多臂赌博机理论中已有广泛研究，但通常优化的是相对于最大均值奖励的累积遗憾。然而，在网络入侵检测等场景中，我们更关注识别出源产生的最极端值。为此，本文提出衡量算法效率的极端遗憾（extreme regret），其对比基准是选择尾部最重的源的最优策略。本文提出了ExtremeHunter算法，给出了理论分析，并在合成与真实世界数据上进行了实证评估。
原文摘要 · Abstract (English)
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.
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