249 citations · 1.2k across the 35 of their papers we have counts for
4 papers · 1 filter
Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning
Paul J. Pritz, Liang Ma, Kin K. Leung
While reinforcement learning has achieved considerable successes in recent years, state-of-the-art models are often still limited by the size of state and action spaces. Model-free…
State Action Separable Reinforcement Learning
Ziyao Zhang, Liang Ma, Kin K. Leung +2
Reinforcement Learning (RL) based methods have seen their paramount successes in solving serial decision-making and control problems in recent years. For conventional RL formulatio…
Overcoming Noisy and Irrelevant Data in Federated Learning
Tiffany Tuor, Shiqiang Wang, Bong Jun Ko +2
Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and comm…
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Pengchao Han, Shiqiang Wang, Kin K. Leung
Federated learning (FL) is an emerging technique for training machine learning models using geographically dispersed data collected by local entities. It includes local computation…