Extreme bandits
For applications like network intrusion detection, this work provides a novel algorithm for detecting extreme values, though it is an incremental contribution to bandit theory.
The paper addresses the problem of allocating limited resources to detect extreme values under limited feedback, proposing the ExtremeHunter algorithm. Empirical evaluations on synthetic and real-world data demonstrate its effectiveness.
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.