2 citations · 2 across the 4 of their papers we have counts for
4 papers
DySTop
Yizhou Shi, Qianpiao Ma, Yan Xu +4
Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However,…
Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation
Qianpiao Ma, Junlong Zhou, Xiangpeng Hou +4
Federated learning (FL) is a new paradigm to train AI models over distributed edge devices (i.e., workers) using their local data, while confronting various challenges including co…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…
Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests
Cyrus Samii, Ye Wang, Junlong Aaron Zhou
In many experimental or quasi-experimental studies, outcomes of interest are only observed for subjects who select (or are selected) to engage in the activity generating the outcom…