1 citations · 1 across the 6 of their papers we have counts for
6 papers
LineageFlow: Flow Matching for High-Fidelity Family-Aware Protein Sequence Generation
Langzhang Liang, Ming Yang, Yi Feng +6
Protein sequence generation for engineering requires samples that are biophysically plausible and, when targeting a family/domain, remain recognizable members while exploring withi…
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…
FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data
Yukun Zhang, Guanzhong Chen, Zenglin Xu +6
Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment. Machine learning (ML) methods c…
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…