8 citations · 29 across the 13 of their papers we have counts for
13 papers
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning
Xinsong Feng, Zihan Yu, Yanhai Xiong +1
Reinforcement learning (RL) has emerged as a promising tool for combinatorial optimization (CO) problems due to its ability to learn fast, effective, and generalizable solutions. N…
Can Reinforcement Learning Solve Asymmetric Combinatorial-Continuous Zero-Sum Games?
Yuheng Li, Panpan Wang, Haipeng Chen
There have been extensive studies on learning in zero-sum games, focusing on the analysis of the existence and algorithmic convergence of Nash equilibrium (NE). Existing studies ma…
Focused ReAct: Improving ReAct through Reiterate and Early Stop
Shuoqiu Li, Han Xu, Haipeng Chen
Large language models (LLMs) have significantly improved their reasoning and decision-making capabilities, as seen in methods like ReAct. However, despite its effectiveness in tack…
Decentralized Online Learning in General-Sum Stackelberg Games
Yaolong Yu, Haipeng Chen
We study an online learning problem in general-sum Stackelberg games, where players act in a decentralized and strategic manner. We study two settings depending on the type of info…
SAC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic
Safa Messaoud, Billel Mokeddem, Zhenghai Xue +4
Learning expressive stochastic policies instead of deterministic ones has been proposed to achieve better stability, sample complexity, and robustness. Notably, in Maximum Entropy…
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…
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,…
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…
AI-driven Prices for Externalities and Sustainability in Production Markets
Panayiotis Danassis, Aris Filos-Ratsikas, Haipeng Chen +2
Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-p…
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…
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…
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…
M2P2: Multimodal Persuasion Prediction using Adaptive Fusion
Chongyang Bai, Haipeng Chen, Srijan Kumar +2
Identifying persuasive speakers in an adversarial environment is a critical task. In a national election, politicians would like to have persuasive speakers campaign on their behal…