193 citations · 244 across the 12 of their papers we have counts for
12 papers
On Learning Necessary and Sufficient Causal Graphs
Hengrui Cai, Yixin Wang, Michael Jordan +1
The causal revolution has stimulated interest in understanding complex relationships in various fields. Most of the existing methods aim to discover causal relationships among all…
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…
OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression
Chunyang Fu, Ge Li, Rui Song +2
In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrea…
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…
A Review on Graph Neural Network Methods in Financial Applications
Jianian Wang, Sheng Zhang, Yanghua Xiao +1
With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations. Due t…
Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning
Ye Shen, Hengrui Cai, Rui Song
Evaluating the performance of an ongoing policy plays a vital role in many areas such as medicine and economics, to provide crucial instructions on the early-stop of the online exp…
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…
ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion
Yaqi Xia, Yan Xia, Wei Li +3
We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed…
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
Ye Liu, Sheng Zhang, Rui Song +2
Open attribute value extraction for emerging entities is an important but challenging task. A lot of previous works formulate the problem as a \textit{question-answering} (QA) task…