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。

多臂赌博机中的通用异常值检测

Generic Outlier Detection in Multi-Armed Bandit

Recommendation systems often face exploration-exploitation tradeoffs: the
system can only learn about the desirability of new options by recommending
them to some user. Such systems can thus be modeled as multi-armed bandit
settings; however, users are self-interested and cannot be made to follow
recommendations. We ask whether exploration can nevertheless be performed in a
way that scrupulously respects agents' interests---i.e., by a system that acts
as a fiduciary. More formally, we introduce a model in which a recommendation
system faces an exploration-exploitation tradeoff under the constraint that it
can never recommend any action that it knows yields lower reward in expectation
than an agent would achieve if it acted alone. Our main contribution is a
positive result: an asymptotically optimal, incentive compatible, and ex-ante
individually rational recommendation algorithm.

本文介绍了一种新的推荐系统模型，其面临探索 - 开发折衷，并可以被建模为多臂老虎机设置，由于用户具有自利性质，因此不能被强制性遵循推荐，作者提出了一种算法以探索为基础，同时考虑到代理人的利益，是一个渐进最优、刺激兼容、先验个体理性的推荐算法。