Towards Causal Classification: A Comprehensive Study on Graph Neural Networks

no code implementations • 27 Jan 2024 • Simi Job, Xiaohui Tao, Taotao Cai, Lin Li, Haoran Xie, Jianming Yong

The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities.

Graph Classification

Exploring Causal Learning through Graph Neural Networks: An In-depth Review

no code implementations • 25 Nov 2023 • Simi Job, Xiaohui Tao, Taotao Cai, Haoran Xie, Lin Li, Jianming Yong, Qing Li

In machine learning, exploring data correlations to predict outcomes is a fundamental task.

Decision Making

Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion

no code implementations • 20 Sep 2023 • Thanveer Shaik, Xiaohui Tao, Lin Li, Niall Higgins, Raj Gururajan, Xujuan Zhou, Jianming Yong

In our study, we propose a novel Clustered FedStack framework based on the previously published Stacked Federated Learning (FedStack) framework.

Clustering Federated Learning +1

Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions

no code implementations • 20 Sep 2023 • Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, Hong-Ning Dai, Jianming Yong

Effective patient monitoring is vital for timely interventions and improved healthcare outcomes.

Hyperparameter Optimization Q-Learning +1

Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence

no code implementations • 18 Sep 2023 • Thanveer Shaik, Xiaohui Tao, Haoran Xie, Lin Li, Jianming Yong, Yuefeng Li

In this study, we propose a novel approach for predicting time-series data using GNN and monitoring with Reinforcement Learning (RL).

Bayesian Optimisation reinforcement-learning +5