12 citations · 12 across the 4 of their papers we have counts for
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cs.LG2026
FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling
Xingyan Chen, Tian Du, Changqiao Xu +4
Federated Learning (FL) faces challenges from client data heterogeneity and resource-constrained mobile devices, which can degrade model accuracy. Personalized Federated Learning (…
cs.LG2026
RPS: Information Elicitation with Reinforcement Prompt Selection
Tao Wang, Jingyao Lu, Xibo Wang +5
Large language models (LLMs) have shown remarkable capabilities in dialogue generation and reasoning, yet their effectiveness in eliciting user-known but concealed information in o…
cs.LG2022
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks
Yu Zhao, Shaopeng Wei, Huaming Du +5
Bi-type multi-relational heterogeneous graph (BMHG) is one of the most common graphs in practice, for example, academic networks, e-commerce user behavior graph and enterprise know…