Yong Shi — accepted AI-conference papers

2025

ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM

Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo +4

EMNLP 2025

Measuring the relevance between user queries and advertisements is a critical task for advertisement (ad) recommendation systems, such as Microsoft Bing Ads and Google Ads. Traditionally, this requires expert data labeling, which is both costly and time-consuming. Recent advances have explored using

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2019

Learning from Label Proportions with Generative Adversarial Networks

Jiabin Liu, Bo Wang, Zhiquan Qi, YingJie Tian, Yong Shi

NeurIPS 2019poster

In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available. Endowed with end-to-end structure, LLP-GAN performs approximation in the lig…

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