471 citations · 2.2k across the 114 of their papers we have counts for
120 papers
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…
Generative AI as Digital Representatives in Collective Decision-Making: A Game-Theoretical Approach
Kexin Chen, Jianwei Huang, Yuan Luo
Generative Artificial Intelligence (GenAI) enables digital representatives to make decisions on behalf of team members in collaborative tasks, but faces challenges in accurately re…
Correlated-Sequence Differential Privacy
Yifan Luo, Meng Zhang, Jin Xu +2
Data streams collected from multiple sources are rarely independent. Values evolve over time and influence one another across sequences. These correlations improve prediction in he…
Trading Vector Data in Vector Databases
Jin Cheng, Xiangxiang Dai, Ningning Ding +2
Vector data trading is essential for cross-domain learning with vector databases, yet it remains largely unexplored. We study this problem under online learning, where sellers face…
FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility
Kangkang Sun, Jun Wu, Minyi Guo +2
Federated Learning (FL) enables collaborative model training without data sharing, yet participants face a fundamental challenge, e.g., simultaneously ensuring fairness across demo…
Personalized Pricing Through Strategic User Profiling in Social Networks
Qinqi Lin, Lingjie Duan, Jianwei Huang
Traditional user profiling techniques rely on browsing history or purchase records to identify users' willingness to pay. This enables sellers to offer personalized prices to profi…
Strategic Prompt Pricing for AIGC Services: A User-Centric Approach
Xiang Li, Bing Luo, Jianwei Huang +1
The rapid growth of AI-generated content (AIGC) services has created an urgent need for effective prompt pricing strategies, yet current approaches overlook users' strategic two-st…
Social Welfare Maximization for Federated Learning with Network Effects
Xiang Li, Yuan Luo, Bing Luo +1
A proper mechanism design can help federated learning (FL) to achieve good social welfare by coordinating self-interested clients through the learning process. However, existing me…
Dual-Role AoI-based Incentive Mechanism for HD map Crowdsourcing
Wentao Ye, Bo Liu, Yuan Luo +1
A high-quality fresh high-definition (HD) map is vital in enhancing transportation efficiency and safety in autonomous driving. Vehicle-based crowdsourcing offers a promising appro…
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
Bing Luo, Wenli Xiao, Shiqiang Wang +2
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communicatio…
Provably Convergent Federated Trilevel Learning
Yang Jiao, Kai Yang, Tiancheng Wu +2
Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine le…
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
Pengchao Han, Shiqiang Wang, Yang Jiao +1
While providing machine learning model as a service to process users' inference requests, online applications can periodically upgrade the model utilizing newly collected data. Fed…
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
Pengchao Han, Xingyan Shi, Jianwei Huang
Knowledge distillation (KD) can enable collaborative learning among distributed clients that have different model architectures and do not share their local data and model paramete…
Information Elicitation from Decentralized Crowd Without Verification
Kexin Chen, Chao Huang, Jianwei Huang
Information Elicitation Without Verification (IEWV) refers to the problem of eliciting high-accuracy solutions from crowd members when the ground truth is unverifiable. A high-accu…
Federated Learning in Competitive EV Charging Market
Chenxi Sun, Chao Huang, Biying Shou +1
Federated Learning (FL) has demonstrated a significant potential to improve the quality of service (QoS) of EV charging stations. While existing studies have primarily focused on d…
Incentive Mechanism Design for Distributed Ensemble Learning
Chao Huang, Pengchao Han, Jianwei Huang
Distributed ensemble learning (DEL) involves training multiple models at distributed learners, and then combining their predictions to improve performance. Existing related studies…
Information Disclosure under Competition in Sharing Systems
Ningning Ding, Zhixuan Fang, Jianwei Huang
Sharing systems have facilitated the redistribution of underused resources by providing convenient online marketplaces for individual sellers and buyers. However, sellers in these…