Fair Algorithms for Infinite and Contextual Bandits
Abstract
We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in Joseph et al. [2016], 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.
Cite
@article{arxiv.1610.09559,
title = {Fair Algorithms for Infinite and Contextual Bandits},
author = {Matthew Joseph and Michael Kearns and Jamie Morgenstern and Seth Neel and Aaron Roth},
journal= {arXiv preprint arXiv:1610.09559},
year = {2017}
}