28 citations · 130 across the 13 of their papers we have counts for
13 papers
PAC-Bayes Information Bottleneck
Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu +3
Understanding the source of the superior generalization ability of NNs remains one of the most important problems in ML research. There have been a series of theoretical works tryi…
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Hengyi Zheng, Rui Wen, Xi Chen +7
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer fro…
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding
Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen +4
Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embeddin…
PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System
Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen +2
Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i.e., mappings) between two KGs. The existing approaches utilize either reasoning-based or semanti…
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen +3
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities. Current methods have explored and utilized the graph structure, the entity names and att…
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management
Zhengxu Hou, Bang Liu, Ruihui Zhao +4
For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the spa…
Lifelong Learning based Disease Diagnosis on Clinical Notes
Zifeng Wang, Yifan Yang, Rui Wen +3
Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model on new tasks usually lea…
Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion
Suyuchen Wang, Ruihui Zhao, Xi Chen +2
Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence. The taxonomy expansion task aims to find a position for a new term in an…
An Industry Evaluation of Embedding-based Entity Alignment
Ziheng Zhang, Jiaoyan Chen, Xi Chen +4
Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of u…
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Zifeng Wang, Xi Chen, Rui Wen +3
Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommen…
Finding Influential Instances for Distantly Supervised Relation Extraction
Zifeng Wang, Rui Wen, Xi Chen +3
Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise. Current works based on attent…
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
Zifeng Wang, Rui Wen, Xi Chen +4
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main chall…
On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond
Zifeng Wang, Xi Chen, Rui Wen +1
Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), wh…