9 papers · 1 filter
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
Zhiyin Yu, Yuchen Mou, Juncheng Yan +17
Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, reinforcement learni…
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
Pengyun Wang, Junyu Luo, Yanxin Shen +5
Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approa…
Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
Junyu Luo, Yuhao Tang, Yiwei Fu +6
Unsupervised Graph Domain Adaptation (UGDA) leverages labeled source domain graphs to achieve effective performance in unlabeled target domains despite distribution shifts. However…
Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval
Junyu Luo, Yusheng Zhao, Xiao Luo +5
Unsupervised efficient domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, while maintaining low storage cost and high…
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation
Pengyun Wang, Yadi Cao, Chris Russell +5
Graph domain adaptation has recently enabled knowledge transfer across different graphs. However, without the semantic information on target graphs, the performance on target graph…
GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation
Junyu Luo, Yiyang Gu, Xiao Luo +5
Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing appro…