44 citations · 102 across the 13 of their papers we have counts for
13 papers
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…
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…
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.…
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
Matthias Gerstgrasser, David C. Parkes
Stackelberg equilibria arise naturally in a range of popular learning problems, such as in security games or indirect mechanism design, and have received increasing attention in th…
Stackelberg POMDP: Learning to Lead via Reinforcement Learning
Gianluca Brero, Alon Eden, Darshan Chakrabarti +6
Many real-world domains--including e-commerce platform design, security planning, and multi-agent coordination--feature leader-follower problems where one decision-maker commits to…
Explainable Reinforcement Learning via Model Transforms
Mira Finkelstein, Lucy Liu, Nitsan Levy Schlot +4
Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult since such agents are often trained in complex environments using highly complex decision ma…