2 citations · 3 across the 13 of their papers we have counts for
13 papers
Learn More with Less: Uncertainty Consistency Guided Query Selection for RLVR
Hao Yi, Yulan Hu, Xin Li +3
Large Language Models (LLMs) have recently improved mathematical reasoning through Reinforcement Learning with Verifiable Reward (RLVR). However, existing RLVR algorithms require l…
Pieceformer: Similarity-Driven Knowledge Transfer via Scalable Graph Transformer in VLSI
Hang Yang, Yusheng Hu, Yong Liu +2
Accurate graph similarity is critical for knowledge transfer in VLSI design, enabling the reuse of prior solutions to reduce engineering effort and turnaround time. We propose Piec…
Towards Reward Fairness in RLHF: From a Resource Allocation Perspective
Sheng Ouyang, Yulan Hu, Ge Chen +3
Rewards serve as proxies for human preferences and play a crucial role in Reinforcement Learning from Human Feedback (RLHF). However, if these rewards are inherently imperfect, exh…
Coarse-to-Fine Process Reward Modeling for Mathematical Reasoning
Yulan Hu, Sheng Ouyang, Jinman Zhao +1
The Process Reward Model (PRM) plays a crucial role in mathematical reasoning tasks, requiring high-quality supervised process data. However, we observe that reasoning steps genera…
GUNDAM: Aligning Large Language Models with Graph Understanding
Sheng Ouyang, Yulan Hu, Ge Chen +1
Large Language Models (LLMs) have achieved impressive results in processing text data, which has sparked interest in applying these models beyond textual data, such as graphs. In t…
TSO: Self-Training with Scaled Preference Optimization
Kaihui Chen, Hao Yi, Qingyang Li +4
Enhancing the conformity of large language models (LLMs) to human preferences remains an ongoing research challenge. Recently, offline approaches such as Direct Preference Optimiza…
Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation
Ge Chen, Yulan Hu, Sheng Ouyang +2
Graph autoencoders (GAEs), as a kind of generative self-supervised learning approach, have shown great potential in recent years. GAEs typically rely on distance-based criteria, su…
Towards Comprehensive Preference Data Collection for Reward Modeling
Yulan Hu, Qingyang Li, Sheng Ouyang +6
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models (LLMs) with human preferences, thereby enhancing the quality of responses gener…
Exploring Task Unification in Graph Representation Learning via Generative Approach
Yulan Hu, Sheng Ouyang, Zhirui Yang +4
Graphs are ubiquitous in real-world scenarios and encompass a diverse range of tasks, from node-, edge-, and graph-level tasks to transfer learning. However, designing specific tas…
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification
Yulan Hu, Sheng Ouyang, Zhirui Yang +1
Class imbalance in graph data presents significant challenges for node classification. While existing methods, such as SMOTE-based approaches, partially mitigate this issue, they s…
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method
Yulan Hu, Sheng Ouyang, Jingyu Liu +6
Graph contrastive learning (GCL) has emerged as a representative graph self-supervised method, achieving significant success. The currently prevalent optimization objective for GCL…
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning
Yulan Hu, Zhirui Yang, Sheng Ouyang +1
Self-Supervised Learning (SSL) has shown significant potential and has garnered increasing interest in graph learning. However, particularly for generative SSL methods, its potenti…
Perfect Alignment May be Poisonous to Graph Contrastive Learning
Jingyu Liu, Huayi Tang, Yong Liu
Graph Contrastive Learning (GCL) aims to learn node representations by aligning positive pairs and separating negative ones. However, few of researchers have focused on the inner l…