144 citations · 268 across the 38 of their papers we have counts for
6 papers · 1 filter
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
Sha Lu, Lin Liu, Kui Yu +3
Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights. This paper introduces Dependency-based Anomaly Detection (DepAD), a…
Computational methods for cancer driver discovery: A survey
Vu Viet Hoang Pham, Lin Liu, Cameron Bracken +3
Motivation: Uncovering the genomic causes of cancer, known as cancer driver genes, is a fundamental task in biomedical research. Cancer driver genes drive the development and progr…
Local search for efficient causal effect estimation
Debo Cheng, Jiuyong Li, Lin Liu +3
Causal effect estimation from observational data is a challenging problem, especially with high dimensional data and in the presence of unobserved variables. The available data-dri…
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…
Towards unique and unbiased causal effect estimation from data with hidden variables
Debo Cheng, Jiuyong Li, Lin Liu +3
Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available.…
Causal query in observational data with hidden variables
Debo Cheng, Jiuyong Li, Lin Liu +3
This paper discusses the problem of causal query in observational data with hidden variables, with the aim of seeking the change of an outcome when "manipulating" a variable while…