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cs.LG2025
GFRIEND: Generative Few-shot Reward Inference through EfficieNt DPO
Yiyang Zhao, Huiyu Bai, Xuejiao Zhao
The ability to train high-performing reward models with few-shot data is critical for enhancing the efficiency and scalability of Reinforcement Learning from Human Feedback (RLHF).…
cs.LG2025
Negative Metric Learning for Graphs
Yiyang Zhao, Chengpei Wu, Lilin Zhang +1
Graph contrastive learning (GCL) often suffers from false negatives, which degrades the performance on downstream tasks. The existing methods addressing the false negative issue us…
cs.LG2023★ 1 cited
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Chao Xue, Wei Liu, Shuai Xie +25
Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to t…