39 citations · 48 across the 4 of their papers we have counts for
6 papers · 1 filter
Editable Graph Neural Network for Node Classifications
Zirui Liu, Zhimeng Jiang, Shaochen Zhong +5
Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-based learning problem, such as credit risk assessment in financial networks and fake news detect…
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Kaixiong Zhou, Ninghao Liu, Fan Yang +5
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many d…
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Kaixiong Zhou, Xiao Huang, Daochen Zha +4
Graph neural networks (GNNs) integrate deep architectures and topological structure modeling in an effective way. However, the performance of existing GNNs would decrease significa…
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation
Yanan Wang, Yong Ge, Li Li +2
Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large num…
Explainable Recommender Systems via Resolving Learning Representations
Ninghao Liu, Yong Ge, Li Li +3
Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing mor…
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning
Xi Liu, Li Li, Ping-Chun Hsieh +3
With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experie…