15 citations · 60 across the 31 of their papers we have counts for
6 papers · 1 filter
Advancing RAN Slicing with Offline Reinforcement Learning
Kun Yang, Shu-ping Yeh, Menglei Zhang +3
Dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing. This technology, cr…
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
Kun Yang, Cong Shen, Jing Yang +2
The recent development of reinforcement learning (RL) has boosted the adoption of online RL for wireless radio resource management (RRM). However, online RL algorithms require dire…
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
Chengshuai Shi, Wei Xiong, Cong Shen +1
Existing theoretical studies on offline reinforcement learning (RL) mostly consider a dataset sampled directly from the target task. In practice, however, data often come from seve…
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
Donghao Li, Ruiquan Huang, Cong Shen +1
This paper investigates conservative exploration in reinforcement learning where the performance of the learning agent is guaranteed to be above a certain threshold throughout the…
Differentially Private Wireless Federated Learning Using Orthogonal Sequences
Xizixiang Wei, Tianhao Wang, Ruiquan Huang +3
We propose a privacy-preserving uplink over-the-air computation (AirComp) method, termed FLORAS, for single-input single-output (SISO) wireless federated learning (FL) systems. Fro…
Reward Teaching for Federated Multi-armed Bandits
Chengshuai Shi, Wei Xiong, Cong Shen +1
Most of the existing federated multi-armed bandits (FMAB) designs are based on the presumption that clients will implement the specified design to collaborate with the server. In r…