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20172023
most citedCalibrated Fairness in Bandits

44 citations · 94 across the 7 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG2023

Deep Contract Design via Discontinuous Networks

Tonghan Wang, Paul Dütting, Dmitry Ivanov +2

Contract design involves a principal who establishes contractual agreements about payments for outcomes that arise from the actions of an agent. In this paper, we initiate the stud…

cs.LG2023

Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces

Omer Nahum, Gali Noti, David Parkes +1

Congestion is a common failure mode of markets, where consumers compete inefficiently on the same subset of goods (e.g., chasing the same small set of properties on a vacation rent…

cs.LG20222 cited

Reinforcement Learning with Stepwise Fairness Constraints

Zhun Deng, He Sun, Zhiwei Steven Wu +2

AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making.…

cs.LG20212 cited

The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning

Stephan Zheng, Alexander Trott, Sunil Srinivasa +2

AI and reinforcement learning (RL) have improved many areas, but are not yet widely adopted in economic policy design, mechanism design, or economics at large. At the same time, cu…

cs.LG201744 cited

Calibrated Fairness in Bandits

Yang Liu, Goran Radanovic, Christos Dimitrakakis +2

We study fairness within the stochastic, \emph{multi-armed bandit} (MAB) decision making framework. We adapt the fairness framework of "treating similar individuals similarly" to t…