2 citations · 2 across the 3 of their papers we have counts for
4 papers
Post-Training in End-to-End Autonomous Driving
Ruining Yang, Muxing Wang, Yixiao Chen +8
End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving. This class…
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
Leo Muxing Wang, Pengkun Yang, Lili Su
Large-scale multi-agent systems are often deployed across wide geographic areas, where agents interact with heterogeneous environments. There is an emerging interest in understandi…
Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling
Xingyan Chen, Tian Du, Mu Wang +5
Federated learning, as a promising distributed learning paradigm, enables collaborative training of a global model across multiple network edge clients without the need for central…
Taming Gradient Variance in Federated Learning with Networked Control Variates
Xingyan Chen, Yaling Liu, Huaming Du +2
Federated learning, a decentralized approach to machine learning, faces significant challenges such as extensive communication overheads, slow convergence, and unstable improvement…