Meritocratic Fairness for Infinite and Contextual Bandits
Meritocratic Fairness for Infinite and Contextual Bandits
复制标题
无限和情境强盗的精英公平
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Aaron Roth
中科院分区：
文献类型：
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作者：
Matthew Joseph;Michael Kearns;Jamie Morgenstern;Seth Neel;Aaron Roth
我们研究线性强盗问题中的公平性。从精英公平的概念引入~\citeJKMR16，我们进行了更精细的分析，一个更一般的问题，实现更好的性能保证与较少的建模假设的数量和结构的可用的选择，以及选择的数量。我们还分析了以前未研究的问题，在无限线性强盗问题的公平性，获得实例相关的遗憾的上界以及下界证明这种实例依赖是必要的。其结果是一个在线线性环境中的精英公平框架，它比当前的艺术水平更强大，更普遍，更现实。
We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in~\citeJKMR16, we carry out a more refined analysis of a more general problem, achieving better performance guarantees with fewer modelling assumptions on the number and structure of available choices as well as the number selected. We also analyze the previously-unstudied question of fairness in infinite linear bandit problems, obtaining instance-dependent regret upper bounds as well as lower bounds demonstrating that this instance-dependence is necessary. The result is a framework for meritocratic fairness in an online linear setting that is substantially more powerful, general, and realistic than the current state of the art.