4 citations · 8 across the 4 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2025
Robustness is Important: Limitations of LLMs for Data Fitting
Hejia Liu, Mochen Yang, Gediminas Adomavicius
Large Language Models (LLMs) are being applied in a wide array of settings, well beyond the typical language-oriented use cases. In particular, LLMs are increasingly used as a plug…
cs.LG2023★ 2 cited
Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective
Yash Travadi, Le Peng, Xuan Bi +2
Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models across data silos collaboratively. Exi…