1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
DARC: Disagreement-Aware Alignment via Risk-Constrained Decoding
Mingxi Zou, Jiaxiang Chen, Junfan Li +4
Preference-based alignment methods (e.g., RLHF, DPO) typically optimize a single scalar objective, implicitly averaging over heterogeneous human preferences. In practice, systemati…
Online Optimization for Learning to Communicate over Time-Correlated Channels
Zheshun Wu, Junfan Li, Zenglin Xu +2
Machine learning techniques have garnered great interest in designing communication systems owing to their capacity in tackling with channel uncertainty. To provide theoretical gua…
On the Necessity of Collaboration for Online Model Selection with Decentralized Data
Junfan Li, Zheshun Wu, Zenglin Xu +1
We consider online model selection with decentralized data over clients, and study the necessity of collaboration among clients. Previous work proposed various federated algori…
Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks
Zheshun Wu, Zenglin Xu, Dun Zeng +2
With the proliferation of intelligent mobile devices in wireless device-to-device (D2D) networks, decentralized federated learning (DFL) has attracted significant interest. Compare…