activity
20232025
most citedA Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2025

MERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking

Shigang Quan, Hailong Tan, Shui Liu +5

Online Travel Platforms (OTPs) have been working on improving their hotel Search & Ranking (S&R) systems that facilitate efficient matching between consumers and hotels. Existing O…

cs.LG20252 cited

A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices

Chen Gong, Rui Xing, Zhenzhe Zheng +1

The demand for machine learning (ML) model training on edge devices is escalating due to data privacy and personalized service needs. However, we observe that current on-device mod…

cs.LG2025

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark

Jianqing Zhang, Xinghao Wu, Yanbing Zhou +7

As AI evolves, collaboration among heterogeneous models helps overcome data scarcity by enabling knowledge transfer across institutions and devices. Traditional Federated Learning…

cs.GT2024

Contextual Generative Auction with Permutation-level Externalities for Online Advertising

Ruitao Zhu, Yangsu Liu, Dagui Chen +7

Online advertising has become a core revenue driver for the internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Tradi…

cs.LG20231 cited

ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation

Yan Zhuang, Zhenzhe Zheng, Yunfeng Shao +3

Pervasive mobile AI applications primarily employ one of the two learning paradigms: cloud-based learning (with powerful large models) or on-device learning (with lightweight small…