In this paper, we study the problem of outlier arm detection in multi-armed
bandit settings, which finds plenty of applications in many high-impact domains
such as finance, healthcare, and online advertising. For this problem, a
learner aims to identify the arms whose expected rewards deviate significantly
from most of the other arms. Different from existing work, we target the
generic outlier arms or outlier arm groups whose expected rewards can be
larger, smaller, or even in between those of normal arms. To this end, we start
by providing a comprehensive definition of such generic outlier arms and
outlier arm groups. Then we propose a novel pulling algorithm named GOLD to
identify such generic outlier arms. It builds a real-time neighborhood graph
based on upper confidence bounds and catches the behavior pattern of outliers
from normal arms. We also analyze its performance from various aspects. In the
experiments conducted on both synthetic and real-world data sets, the proposed
algorithm achieves 98 % accuracy while saving 83 % exploration cost on average
compared with state-of-the-art techniques.

本文研究在多臂老虎机中检测异常臂的问题，并提出了一个名为 GOLD 的新型拉动算法，通过实时构建邻域图形的方式实现对可能存在的异常臂的鲁棒识别，实验结果表明该算法相对于最先进的技术可平均节省 83％的探索成本，并在准确性上达到了 98％的表现。