78 citations · 333 across the 57 of their papers we have counts for
5 papers · 1 filter
Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences
Tanner Fiez, Shreyas Sekar, Liyuan Zheng +1
The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge. We propose a multi-armed…
Adaptive Incentive Design
Lillian J. Ratliff, Tanner Fiez
We apply control theoretic and optimization techniques to adaptively design incentives. In particular, we consider the problem of a planner with an objective that depends on data f…
On Gradient-Based Learning in Continuous Games
Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry
We formulate a general framework for competitive gradient-based learning that encompasses a wide breadth of multi-agent learning algorithms, and analyze the limiting behavior of co…
Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback
Tanner Fiez, Shreyas Sekar, Lillian J. Ratliff
We study a multi-armed bandit problem in a dynamic environment where arm rewards evolve in a correlated fashion according to a Markov chain. Different than much of the work on rela…
Towards a Socially Optimal Multi-Modal Routing Platform
Chinmaya Samal, Liyuan Zheng, Fangzhou Sun +2
The increasing rate of urbanization has added pressure on the already constrained transportation networks in our communities. Ride-sharing platforms such as Uber and Lyft are becom…