4 citations · 7 across the 7 of their papers we have counts for
5 papers · 1 filter
Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
Simi Job, Xiaohui Tao, Taotao Cai +3
Graph data is becoming increasingly prevalent due to the growing demand for relational insights in AI across various domains. Organizations regularly use graph data to solve comple…
Causal Neighbourhood Learning for Invariant Graph Representations
Simi Job, Xiaohui Tao, Taotao Cai +2
Graph data often contain noisy and spurious correlations that mask the true causal relationships, which are essential for enabling graph models to make predictions based on the und…
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
Simi Job, Xiaohui Tao, Taotao Cai +3
The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approxima…
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
Simi Job, Xiaohui Tao, Taotao Cai +4
In machine learning, exploring data correlations to predict outcomes is a fundamental task. Recognizing causal relationships embedded within data is pivotal for a comprehensive und…
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Machine Unlearning is an emerging field that addresses data privacy issues by enabling the removal of private or irrelevant data from the Machine Learning process. Challenges relat…