381 citations · 2.3k across the 89 of their papers we have counts for
4 papers · 2 filters
Fairness in Reinforcement Learning
Shahin Jabbari, Matthew Joseph, Michael Kearns +2
We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards. Our fairness constraint requir…
Fair Algorithms for Infinite and Contextual Bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern +2
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 ge…
Fairness in Learning: Classic and Contextual Bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern +1
We introduce the study of fairness in multi-armed bandit problems. Our fairness definition can be interpreted as demanding that given a pool of applicants (say, for college admissi…
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing
Ryan Rogers, Aaron Roth, Adam Smith +1
In this paper, we initiate a principled study of how the generalization properties of approximate differential privacy can be used to perform adaptive hypothesis testing, while giv…