2 citations · 2 across the 7 of their papers we have counts for
7 papers
Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing
Shugang Hao, Lingjie Duan
To better serve users' demands in mobile applications (e.g., navigation), mobile crowdsourcing platforms can iteratively align large language model (LLM)-generated content (e.g., A…
To Theoretically Understand Transformer-Based In-Context Learning for Optimizing CSMA
Shugang Hao, Hongbo Li, Lingjie Duan
The binary exponential backoff scheme is widely used in WiFi 7 and still incurs poor throughput performance under dynamic channel environments. Recent model-based approaches (e.g.,…
Online Learning from Strategic Human Feedback in LLM Fine-Tuning
Shugang Hao, Lingjie Duan
Reinforcement learning from human feedback (RLHF) has become an essential step in fine-tuning large language models (LLMs) to align them with human preferences. However, human labe…
Algorithm Design for Continual Learning in IoT Networks
Shugang Hao, Lingjie Duan
Continual learning (CL) is a new online learning technique over sequentially generated streaming data from different tasks, aiming to maintain a small forgetting loss on previously…
To Save Mobile Crowdsourcing from Cheap-talk: A Game Theoretic Learning Approach
Shugang Hao, Lingjie Duan
Today mobile crowdsourcing platforms invite users to provide anonymous reviews about service experiences, yet many reviews are found biased to be extremely positive or negative. Th…
To Help or Disturb: Introduction of Crowdsourced WiFi to 5G Networks
Shugang Hao, Lingjie Duan
After upgrading to 5G, a network operator still faces congestion when providing the ubiquitous wireless service to the crowd. To meet users' ever-increasing demand, some other oper…
Regulating Competition in Age of Information under Network Externalities
Shugang Hao, Lingjie Duan
Online content platforms are concerned about the freshness of their content updates to their end customers, and increasingly more platforms now invite and pay the crowd to sample r…