5 papers · 1 filter
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…
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…
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Thanveer Shaik, Xiaohui Tao, Haoran Xie +3
Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more…