4 citations · 11 across the 9 of their papers we have counts for
9 papers
SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory
Xingtao Zhao, Hao Peng, Dingli Su +4
Reliable uncertainty quantification (UQ) is essential for deploying large language models (LLMs) in safety-critical scenarios, as it enables them to abstain from responding when un…
RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction
Yingguang Yang, Xianghua Zeng, Qi Wu +5
Social networks have become a crucial source of real-time information for individuals. The influence of social bots within these platforms has garnered considerable attention from…
Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learning
Xianghua Zeng, Hao Peng, Angsheng Li +1
Diffusion-based generative methods have shown promising potential for modeling trajectories from offline reinforcement learning (RL) datasets, and hierarchical diffusion has been i…
Robustness Evaluation of Graph-based News Detection Using Network Structural Information
Xianghua Zeng, Hao Peng, Angsheng Li
Although Graph Neural Networks (GNNs) have shown promising potential in fake news detection, they remain highly vulnerable to adversarial manipulations within social networks. Exis…
Effective Exploration Based on the Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li
Traditional information theory provides a valuable foundation for Reinforcement Learning, particularly through representation learning and entropy maximization for agent exploratio…
Hierarchical Decision Making Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Dingli Su +1
Hierarchical Reinforcement Learning (HRL) is a promising approach for managing task complexity across multiple levels of abstraction and accelerating long-horizon agent exploration…
Adversarial Socialbots Modeling Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li
The importance of effective detection is underscored by the fact that socialbots imitate human behavior to propagate misinformation, leading to an ongoing competition between socia…
Hierarchical State Abstraction Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li +3
State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations. Nevertheless, recent approaches focus…
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li
Role-based learning is a promising approach to improving the performance of Multi-Agent Reinforcement Learning (MARL). Nevertheless, without manual assistance, current role-based m…