1 citations · 2 across the 9 of their papers we have counts for
15 papers
Masked Random Noise for Communication Efficient Federated Learning
Shiwei Li, Yingyi Cheng, Haozhao Wang +7
Federated learning is a promising distributed training paradigm that effectively safeguards data privacy. However, it may involve significant communication costs, which hinders tra…
End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling
Zexu Sun, Hao Yang, Dugang Liu +3
In modern online platforms, incentives are essential factors that enhance user engagement and increase platform revenue. Over recent years, uplift modeling has been introduced as a…
OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction
Yunpeng Weng, Xing Tang, Zhenhao Xu +4
Customer Lifetime Value (CLTV) prediction is a critical task in business applications. Accurately predicting CLTV is challenging in real-world business scenarios, as the distributi…
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Shiwei Li, Wenchao Xu, Haozhao Wang +7
Federated learning is a promising distributed machine learning paradigm that can effectively exploit large-scale data without exposing users' privacy. However, it may incur signifi…
Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks
Ziqiang Cui, Xing Tang, Yang Qiao +4
Estimating the individual treatment effect (ITE) from observational data is a crucial research topic that holds significant value across multiple domains. How to identify hidden co…
Expected Transaction Value Optimization for Precise Marketing in FinTech Platforms
Yunpeng Weng, Xing Tang, Liang Chen +2
FinTech platforms facilitated by digital payments are watching growth rapidly, which enable the distribution of mutual funds personalized to individual investors via mobile Apps. A…