22 citations · 38 across the 6 of their papers we have counts for
6 papers
Learning to Optimise Climate Sensor Placement using a Transformer
Chen Wang, Victoria Huang, Gang Chen +3
The optimal placement of sensors for environmental monitoring and disaster management is a challenging problem due to its NP-hard nature. Traditional methods for sensor placement i…
Deep Metric Tensor Regularized Policy Gradient
Gang Chen, Victoria Huang
Policy gradient algorithms are an important family of deep reinforcement learning techniques. Many past research endeavors focused on using the first-order policy gradient informat…
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients
Victoria Huang, Shaleeza Sohail, Michael Mayo +4
Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training da…
Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training
Gang Chen, Victoria Huang
Actor-critic deep reinforcement learning (DRL) algorithms have recently achieved prominent success in tackling various challenging reinforcement learning (RL) problems, particularl…
Effective Scheduling Function Design in SDN through Deep Reinforcement Learning
Huang Victoria, Chen Gang, Fu Qiang
Recent research on Software-Defined Networking (SDN) strongly promotes the adoption of distributed controller architectures. To achieve high network performance, designing a schedu…
Optimizing Controller Placement for Software-Defined Networks
Victoria Huang, Gang Chen, Qiang Fu +1
Controller placement problem (CPP) is a key issue for Software-Defined Networking (SDN) with distributed controller architectures. This problem aims to determine a suitable number…