Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising

no code implementations • 7 Jun 2021 • Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, YiQing Wang, Dagui Chen, Jian Xu, Fan Wu, Guihai Chen, Xiaoqiang Zhu

In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.

Data-Free Evaluation of User Contributions in Federated Learning

no code implementations • 24 Aug 2021 • Hongtao Lv, Zhenzhe Zheng, Tie Luo, Fan Wu, Shaojie Tang, Lifeng Hua, Rongfei Jia, Chengfei Lv

We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset.

Federated Learning Product Recommendation

Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach

no code implementations • 15 Feb 2023 • Shijing Yuan, Hongze Liu, Hongtao Lv, Zhanbo Feng, Jie Li, Hongyang Chen, Chentao Wu

To overcome these limitations, we propose a novel adaptive mechanism for cross-silo FL, towards incentivizing organizations to contribute data to maximize their long-term payoffs in a real dynamic training environment.

Federated Learning Multi-agent Reinforcement Learning