8 citations · 15 across the 30 of their papers we have counts for
6 papers · 1 filter
EchoRL: Reinforcement Learning via Rollout Echoing
Jinhe Bi, Aniri, Minglai Yang +9
Reinforcement Learning with Verifiable Rewards is an effective route for post-training to strengthen the reasoning capability of large language models. However, as training proceed…
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7
Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambiguity, but its success relies o…
Federated Graph Semantic and Structural Learning
Wenke Huang, Guancheng Wan, Mang Ye +1
Federated graph learning collaboratively learns a global graph neural network with distributed graphs, where the non-independent and identically distributed property is one of the…
Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity
Yuhang Chen, Wenke Huang, Mang Ye
Federated learning (FL) has emerged as a new paradigm for privacy-preserving collaborative training. Under domain skew, the current FL approaches are biased and face two fairness p…
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
Wenke Huang, Mang Ye, Zekun Shi +4
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx…
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning
Wenke Huang, Mang Ye, Zekun Shi +1
Federated learning is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating on private data. Model heteroge…