1 citations · 3 across the 6 of their papers we have counts for
6 papers
Multi-agent Policy Reciprocity with Theoretical Guarantee
Haozhi Wang, Yinchuan Li, Qing Wang +2
Modern multi-agent reinforcement learning (RL) algorithms hold great potential for solving a variety of real-world problems. However, they do not fully exploit cross-agent knowledg…
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies
Haozhi Wang, Qing Wang, Yunfeng Shao +3
Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize polic…
Tensor Decomposition based Personalized Federated Learning
Qing Wang, Jing Jin, Xiaofeng Liu +3
Federated learning (FL) is a new distributed machine learning framework that can achieve reliably collaborative training without collecting users' private data. However, due to FL'…
Sparse Federated Learning with Hierarchical Personalized Models
Xiaofeng Liu, Qing Wang, Yunfeng Shao +1
Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collecting users' private data. Its excellent privacy security potential promotes a wid…
Multiple Intelligent Reflecting Surface aided Multi-user Weighted Sum-Rate Maximization using Manifold Optimization
Liyue Zhang, Qing Wang, Haozhi Wang
Intelligent reflecting surface (IRS) are able to amend radio propagation condition tasks on account of its functional properties in phase shift optimizing. In fact, there exists ge…
Sparse Personalized Federated Learning
Xiaofeng Liu, Yinchuan Li, Qing Wang +3
Federated Learning (FL) is a collaborative machine learning technique to train a global model without obtaining clients' private data. The main challenges in FL are statistical div…