10 citations · 29 across the 22 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…
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo +5
Analysis of large-scale sequential data has been one of the most crucial tasks in areas such as bioinformatics, text, and audio mining. Existing string kernels, however, either (i)…
SENSE: Semantically Enhanced Node Sequence Embedding
Swati Rallapalli, Liang Ma, Mudhakar Srivatsa +4
Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single grap…