84 citations · 186 across the 28 of their papers we have counts for
9 papers · 1 filter
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
Junyu Luo, Xiao Luo, Kaize Ding +3
Supervised fine-tuning (SFT) plays a crucial role in adapting large language models (LLMs) to specific domains or tasks. However, as demonstrated by empirical experiments, the coll…
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…
Semi-supervised Fine-tuning for Large Language Models
Junyu Luo, Xiao Luo, Xiusi Chen +3
Supervised fine-tuning (SFT) is crucial in adapting large language model (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practic…
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…
Automate Strategy Finding with LLM in Quant Investment
Zhizhuo Kou, Holam Yu, Junyu Luo +7
We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our a…
Rank and Align: Towards Effective Source-free Graph Domain Adaptation
Junyu Luo, Zhiping Xiao, Yifan Wang +5
Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to p…
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…
Towards Graph Contrastive Learning: A Survey and Beyond
Wei Ju, Yifan Wang, Yifang Qin +10
In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to i…
A Survey of Data-Efficient Graph Learning
Wei Ju, Siyu Yi, Yifan Wang +4
Graph-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems. While graph neural network…