519 citations · 1.7k across the 26 of their papers we have counts for
19 papers · 1 filter
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Ke Tu, Peng Cui, Daixin Wang +4
Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relati…
Kernelized Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui +2
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-…
Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui +2
Machine learning algorithms with empirical risk minimization usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training d…
Deep Stable Learning for Out-Of-Distribution Generalization
Xingxuan Zhang, Peng Cui, Renzhe Xu +3
Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Th…
Accurate and Reliable Forecasting using Stochastic Differential Equations
Peng Cui, Zhijie Deng, Wenbo Hu +1
It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments. Although a lot of efforts have been made,…
Interpreting and Unifying Graph Neural Networks with An Optimization Framework
Meiqi Zhu, Xiao Wang, Chuan Shi +2
Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism which has b…