8 citations · 8 across the 7 of their papers we have counts for
11 papers
S2T-RLHF: Hierarchical Credit Assignment for Stable Preference-Based RLHF
Wei Chen, Guanghui Zhu, Yafei Li +2
Reinforcement learning from human feedback (RLHF) with preference-based reward models often exhibits unstable training dynamics. A key contributing factor is that standard RLHF rel…
DelvePO: Direction-Guided Self-Evolving Framework for Flexible Prompt Optimization
Tao Tao, Guanghui Zhu, Lang Guo +3
Prompt Optimization has emerged as a crucial approach due to its capabilities in steering Large Language Models to solve various tasks. However, current works mainly rely on the ra…
BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering
Guanghui Zhu, Xin Fang, Feng Cheng +4
Machine learning has been making great success in many application areas. However, for the non-expert practitioners, it is always very challenging to address a machine learning tas…
PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs
Tongzhou Yu, Zhuhao Zhang, Guanghui Zhu +3
Parameter Efficient Fine-Tuning (PEFT) methods have emerged as effective and promising approaches for fine-tuning pre-trained language models. Compared with Full parameter Fine-Tun…
SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
Guanghui Zhu, Zipeng Ji, Jingyan Chen +3
GNAS (Graph Neural Architecture Search) has demonstrated great effectiveness in automatically designing the optimal graph neural architectures for multiple downstream tasks, such a…
Simple and Efficient Partial Graph Adversarial Attack: A New Perspective
Guanghui Zhu, Mengyu Chen, Chunfeng Yuan +1
As the study of graph neural networks becomes more intensive and comprehensive, their robustness and security have received great research interest. The existing global attack meth…