activity
20172022
most citedArtificial Intelligence for Social Good

68 citations · 146 across the 10 of their papers we have counts for

collaborators

19 papers

cs.GT20226 cited

Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality

Jibang Wu, Weiran Shen, Fei Fang +1

Optimizing strategic decisions (a.k.a. computing equilibrium) is key to the success of many non-cooperative multi-agent applications. However, in many real-world situations, we may…

cs.AI2022

Ranked Prioritization of Groups in Combinatorial Bandit Allocation

Lily Xu, Arpita Biswas, Fei Fang +1

Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land. Combinatorial bandits have…

cs.LG20218 cited

Robust Reinforcement Learning Under Minimax Regret for Green Security

Lily Xu, Andrew Perrault, Fei Fang +2

Green security domains feature defenders who plan patrols in the face of uncertainty about the adversarial behavior of poachers, illegal loggers, and illegal fishers. Importantly,…

cs.AI2020

Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

Steven Jecmen, Hanrui Zhang, Ryan Liu +3

We consider three important challenges in conference peer review: (i) reviewers maliciously attempting to get assigned to certain papers to provide positive reviews, possibly as pa…

cs.LG2020

Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning

Qian Long, Zihan Zhou, Abhibav Gupta +3

In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the…

cs.GT20202 cited

Green Security Game with Community Engagement

Taoan Huang, Weiran Shen, David Zeng +3

While game-theoretic models and algorithms have been developed to combat illegal activities, such as poaching and over-fishing, in green security domains, none of the existing work…