4 citations · 10 across the 11 of their papers we have counts for
13 papers
Disentangled Instrumental Variables for Causal Inference with Networked Observational Data
Zhirong Huang, Debo Cheng, Guixian Zhang +3
Instrumental variables (IVs) are crucial for addressing unobservable confounders, yet their stringent exogeneity assumptions pose significant challenges in networked data. Existing…
DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
Qingfeng Chen, Haojin Zeng, Jingyi Jie +2
With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to identify entire graph…
Harnessing LLM for Noise-Robust Cognitive Diagnosis in Web-Based Intelligent Education Systems
Guixian Zhang, Guan Yuan, Ziqi Xu +4
Cognitive diagnostics in the Web-based Intelligent Education System (WIES) aims to assess students' mastery of knowledge concepts from heterogeneous, noisy interactions. Recent wor…
Efficient Adaptive Label Refinement for Label Noise Learning
Wenzhen Zhang, Debo Cheng, Guangquan Lu +3
Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined crit…
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
Chengyu Li, Debo Cheng, Guixian Zhang +2
Graph Neural Networks (GNNs) have demonstrated strong performance in graph representation learning across various real-world applications. However, they often produce biased predic…
Towards Fair Graph Representation Learning in Social Networks
Guixian Zhang, Guan Yuan, Debo Cheng +3
With the widespread use of Graph Neural Networks (GNNs) for representation learning from network data, the fairness of GNN models has raised great attention lately. Fair GNNs aim t…
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems
Zhirong Huang, Shichao Zhang, Debo Cheng +3
In recommender systems, popularity and conformity biases undermine recommender effectiveness by disproportionately favouring popular items, leading to their over-representation in…
Community-Centric Graph Unlearning
Yi Li, Shichao Zhang, Guixian Zhang +1
Graph unlearning technology has become increasingly important since the advent of the `right to be forgotten' and the growing concerns about the privacy and security of artificial…
Interaction-Data-guided Conditional Instrumental Variables for Debiasing Recommender Systems
Zhirong Huang, Debo Cheng, Jiuyong Li +3
It is often challenging to identify a valid instrumental variable (IV), although the IV methods have been regarded as effective tools of addressing the confounding bias introduced…
Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data
Debo Cheng, Jiuyong Li, Lin Liu +3
Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confo…
Sufficient Dimension Reduction for Average Causal Effect Estimation
Debo Cheng, Jiuyong Li, Lin Liu +1
Having a large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covaria…
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…
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…