论文标题
多用户系统的匿名土匪
Anonymous Bandits for Multi-User Systems
论文作者
论文摘要
在这项工作中，我们介绍并研究了与多个提供用户匿名的用户的系统中在线学习的新框架。具体来说，我们扩展了匪徒的概念，以遵守标准$ k $匿名的约束，要求每个观察值是至少$ k $用户的奖励的汇总。这提供了一个简单而有效的框架，可以在线学习用户的聚类，而无需观察任何用户的个人决定。我们启动对匿名土匪的研究，并为此设置提供第一个Sublinear后悔算法和下限。
In this work, we present and study a new framework for online learning in systems with multiple users that provide user anonymity. Specifically, we extend the notion of bandits to obey the standard $k$-anonymity constraint by requiring each observation to be an aggregation of rewards for at least $k$ users. This provides a simple yet effective framework where one can learn a clustering of users in an online fashion without observing any user's individual decision. We initiate the study of anonymous bandits and provide the first sublinear regret algorithms and lower bounds for this setting.
