6 papers
Neural Tree Collaborative Filtering: Rethinking Graph Collaborative Filtering as Tree Collaborative Filtering with Curvature-Aware Propagation Depth
Jinfeng Xu, Zheyu Chen, Ziyue Peng +5
Graph Collaborative Filtering (GCF) has become the dominant paradigm in modern recommender systems by modeling user-item interactions as a bipartite graph and propagating embedding…
Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
Wenhao Yuan, Chenchen Lin, Jian Chen +3
Federated Learning (FL) emerged as a promising distributed machine learning paradigm. However, extending FL to the class incremental learning scenarios introduces unique challenges…
Belief-Guided Inference Control for Large Language Model Services via Verifiable Observations
Wenhao Yuan, Chenchen Lin, Jian Chen +3
In black-box large language model (LLM) services, response reliability is often only partially observable at decision time, while stronger inference pathways incur substantial comp…
DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification
Jian Chen, Yuzhu Hu, Xiaoyan Yuan +6
Irregular Medical Time Series play a critical role in the clinical domain to better understand the patient's condition. However, inherent irregularity arising from heterogeneous sa…
Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
Wenhao Yuan, Chenchen Lin, Jian Chen +3
In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory. However, coherent reasoning can still…
A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning
Wenhao Yuan, Xuehe Wang
This paper aims to design a Privacy-aware Client Sampling framework in Federated learning, named FedPCS, to tackle the heterogeneous client sampling issues and improve model perfor…