activity
20192021
most citedGraph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data

25 citations · 57 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG202125 cited

Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data

Zhixuan Chu, Stephen L. Rathbun, Sheng Li

Treatment effect estimation from observational data is a critical research topic across many domains. The foremost challenge in treatment effect estimation is how to capture hidden…

cs.CL20212 cited

Edge: Enriching Knowledge Graph Embeddings with External Text

Saed Rezayi, Handong Zhao, Sungchul Kim +3

Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the…

cs.CV2020

Collaborative Attention Mechanism for Multi-View Action Recognition

Yue Bai, Zhiqiang Tao, Lichen Wang +3

Multi-view action recognition (MVAR) leverages complementary temporal information from different views to improve the learning performance. Obtaining informative view-specific repr…

cs.CV20206 cited

Stereotype-Free Classification of Fictitious Faces

Mohammadhossein Toutiaee, Soheyla Amirian, John A. Miller +1

Equal Opportunity and Fairness are receiving increasing attention in artificial intelligence. Stereotyping is another source of discrimination, which yet has been unstudied in lite…

stat.ME202024 cited

A Survey on Causal Inference

Liuyi Yao, Zhixuan Chu, Sheng Li +3

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics, for decades. Nowadays, estimating c…

cs.LG2019

Learning Robust Data Representation: A Knowledge Flow Perspective

Zhengming Ding, Ming Shao, Handong Zhao +1

It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompletenes…