58 citations · 92 across the 19 of their papers we have counts for
11 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…
Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions
Sadia Qureshi, Thanveer Shaik, Xiaohui Tao +4
The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' bec…
Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing
Matthew Squires, Xiaohui Tao, Soman Elangovan +4
Suicide is a prominent issue in society. Unfortunately, many people at risk for suicide do not receive the support required. Barriers to people receiving support include social sti…
DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks
Matthew Squires, Xiaohui Tao, Soman Elangovan +5
Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent.…
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…
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Reinforcement learning has been increasingly applied in monitoring applications because of its ability to learn from previous experiences and can make adaptive decisions. However,…
Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Effective patient monitoring is vital for timely interventions and improved healthcare outcomes. Traditional monitoring systems often struggle to handle complex, dynamic environmen…
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…
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted…