28 citations · 60 across the 4 of their papers we have counts for
5 papers · 1 filter
Any Part of Bayesian Network Structure Learning
Zhaolong Ling, Kui Yu, Hao Wang +2
We study an interesting and challenging problem, learning any part of a Bayesian network (BN) structure. In this challenge, it will be computationally inefficient using existing gl…
Towards Efficient Local Causal Structure Learning
Shuai Yang, Hao Wang, Kui Yu +2
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes…
Learning causal representations for robust domain adaptation
Shuai Yang, Kui Yu, Fuyuan Cao +3
Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all exist…
A general framework for causal classification
Jiuyong Li, Weijia Zhang, Lin Liu +3
In many applications, there is a need to predict the effect of an intervention on different individuals from data. For example, which customers are persuadable by a product promoti…
Causality-based Feature Selection: Methods and Evaluations
Kui Yu, Xianjie Guo, Lin Liu +4
Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between p…