194 citations · 998 across the 36 of their papers we have counts for
5 papers · 1 filter
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…
Learning Simple Auctions
Jamie Morgenstern, Tim Roughgarden
We present a general framework for proving polynomial sample complexity bounds for the problem of learning from samples the best auction in a class of "simple" auctions. Our framew…
Simple Mechanisms For Agents With Complements
Michal Feldman, Ophir Friedler, Jamie Morgenstern +1
We study the efficiency of simple auctions in the presence of complements. [DMSW15] introduced the single-bid auction, and showed that it has a price of anarchy (PoA) of $O(\log m)…