Anonymous Bandits for Multi-User Systems

被引:0
|
作者
Esfandiari, Hossein [1 ]
Mirrokni, Vahab [1 ]
Schneider, Jon [1 ]
机构
[1] Google Res, Mountain View, CA 94043 USA
来源
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35, NEURIPS 2022 | 2022年
关键词
REGRET BOUNDS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页数:13
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