471 citations · 2.2k across the 114 of their papers we have counts for
8 papers · 1 filter
Does AI Help or Harm? Endogenous Information Acquisition with AI Advice
Kexin Chen, Tao Lin, Jianwei Huang
AI advice increasingly enters human decision workflows before costly information acquisition and final action. This paper studies how such advice affects decision quality, measured…
CoE: Collaborative Entropy for Uncertainty Quantification in Agentic Multi-LLM Systems
Kangkang Sun, Jun Wu, Jianhua Li +3
Uncertainty estimation in multi-LLM systems remains largely single-model-centric: existing methods quantify uncertainty within each model but do not adequately capture semantic dis…
Recruiting Heterogeneous Crowdsource Vehicles for Updating a High-definition Map
Wentao Ye, Yuan Luo, Bo Liu +1
The high-definition map is a cornerstone of autonomous driving. Unlike constructing a costly fleet of mapping vehicles, the crowdsourcing paradigm is a cost-effective way to keep a…
Efficient and Cost-effective Vehicle Recruitment for HD Map Crowdsourcing
Wentao Ye, Yuan Luo, Bo Liu +1
The high-definition (HD) map is a cornerstone of autonomous driving. The crowdsourcing paradigm is a cost-effective way to keep an HD map up-to-date. Current HD map crowdsourcing m…
Learning From Social Interactions: Personalized Pricing and Buyer Manipulation
Qinqi Lin, Lingjie Duan, Jianwei Huang
As the sociological theory of homophily suggests, people tend to interact with those of similar preferences. Motivated by this well-established phenomenon, today's online sellers,…
Governing AI Forgetting: Auditing for Machine Unlearning Compliance
Qinqi Lin, Ningning Ding, Lingjie Duan +1
Despite legal mandates for the right to be forgotten, AI operators routinely fail to comply with data deletion requests. While machine unlearning (MU) provides a technical solution…
Machine Learning Model Trading with Verification under Information Asymmetry
Xiang Li, Jianwei Huang, Kai Yang +1
Machine learning (ML) model trading, known for its role in protecting data privacy, faces a major challenge: information asymmetry. This issue can lead to model deception, a proble…
Mechanism Design for Federated Learning with Non-Monotonic Network Effects
Xiang Li, Bing Luo, Jianwei Huang +1
Mechanism design is pivotal to federated learning (FL) for maximizing social welfare by coordinating self-interested clients. Existing mechanisms, however, often overlook the netwo…