57 citations · 217 across the 32 of their papers we have counts for
10 papers · 1 filter
Instrumental Variables in Causal Inference and Machine Learning: A Survey
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +1
Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. T…
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
Qi Tian, Kun Kuang, Kelu Jiang +3
The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet. However, it also means that users' private data may be co…
Confounder Balancing for Instrumental Variable Regression with Latent Variable
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +2
This paper studies the confounding effects from the unmeasured confounders and the imbalance of observed confounders in IV regression and aims at unbiased causal effect estimation.…
Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples
Chengyuan Liu, Leilei Gan, Kun Kuang +1
The aim of Logic2Text is to generate controllable and faithful texts conditioned on tables and logical forms, which not only requires a deep understanding of the tables and logical…
Learning Individual Treatment Effects under Heterogeneous Interference in Networks
Ziyu Zhao, Yuqi Bai, Kun Kuang +2
Estimates of individual treatment effects from networked observational data are attracting increasing attention these days. One major challenge in network scenarios is the violatio…
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization
Zheqi Lv, Wenqiao Zhang, Shengyu Zhang +8
Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. It aims to improve the generalization ability of…