1 citations · 3 across the 6 of their papers we have counts for
Showing 2022Show all
3 papers · 1 filter
cs.AI2022
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…
cs.LG2022★ 1 cited
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'…
cs.LG2022★ 1 cited
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…