78 citations · 165 across the 14 of their papers we have counts for
3 papers · 1 filter
Quantifying the Burden of Exploration and the Unfairness of Free Riding
Christopher Jung, Sampath Kannan, Neil Lutz
We consider the multi-armed bandit setting with a twist. Rather than having just one decision maker deciding which arm to pull in each round, we have different decision makers…
Fair Algorithms for Learning in Allocation Problems
Hadi Elzayn, Shahin Jabbari, Christopher Jung +4
Settings such as lending and policing can be modeled by a centralized agent allocating a resource (loans or police officers) amongst several groups, in order to maximize some objec…
Online Learning with an Unknown Fairness Metric
Stephen Gillen, Christopher Jung, Michael Kearns +1
We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown simil…