activity
20202024
most citedRobust Reinforcement Learning Under Minimax Regret for Green Security

8 citations · 21 across the 7 of their papers we have counts for

collaborators

6 papers

cs.LG2022

Learning Correlated Stackelberg Equilibrium in General-Sum Multi-Leader-Single-Follower Games

Yaolong Yu, Haifeng Xu, Haipeng Chen

Many real-world strategic games involve interactions between multiple players. We study a hierarchical multi-player game structure, where players with asymmetric roles can be separ…

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.SI20211 cited

Contingency-Aware Influence Maximization: A Reinforcement Learning Approach

Haipeng Chen, Wei Qiu, Han-Ching Ou +2

The influence maximization (IM) problem aims at finding a subset of seed nodes in a social network that maximize the spread of influence. In this study, we focus on a sub-class of…

cs.LG20212 cited

Active Screening for Recurrent Diseases: A Reinforcement Learning Approach

Han-Ching Ou, Haipeng Chen, Shahin Jabbari +1

Active screening is a common approach in controlling the spread of recurring infectious diseases such as tuberculosis and influenza. In this approach, health workers periodically s…

cs.LG2020

EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation

Qi Zhou, Haipeng Chen, Yitao Zheng +1

As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer…

cs.AI20208 cited

Learning Behaviors with Uncertain Human Feedback

Xu He, Haipeng Chen, Bo An

Human feedback is widely used to train agents in many domains. However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the op…