23 citations · 36 across the 6 of their papers we have counts for
8 papers
Locally Aggregated Feature Attribution on Natural Language Model Understanding
Sheng Zhang, Jin Wang, Haitao Jiang +1
With the growing popularity of deep-learning models, model understanding becomes more important. Much effort has been devoted to demystify deep neural networks for better interpret…
Rule Mining over Knowledge Graphs via Reinforcement Learning
Lihan Chen, Sihang Jiang, Jingping Liu +6
Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related resea…
A Probit Tensor Factorization Model For Relational Learning
Ye Liu, Rui Song, Wenbin Lu +1
With the proliferation of knowledge graphs, modeling data with complex multirelational structure has gained increasing attention in the area of statistical relational learning. One…
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
Runzhe Wan, Lin Ge, Rui Song
How to explore efficiently is a central problem in multi-armed bandits. In this paper, we introduce the metadata-based multi-task bandit problem, where the agent needs to solve a l…
Periodic-GP: Learning Periodic World with Gaussian Process Bandits
Hengrui Cai, Zhihao Cen, Ling Leng +1
We consider the sequential decision optimization on the periodic environment, that occurs in a wide variety of real-world applications when the data involves seasonality, such as t…
Topological Regularization for Graph Neural Networks Augmentation
Rui Song, Fausto Giunchiglia, Ke Zhao +1
The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision. In this paper, we propose a featur…