18 citations · 41 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion
Thanveer Shaik, Xiaohui Tao, Lin Li +4
Federated Learning (FL) is currently one of the most popular technologies in the field of Artificial Intelligence (AI) due to its collaborative learning and ability to preserve cli…
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…
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…