98 citations · 434 across the 26 of their papers we have counts for
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cs.LG2023
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
Yunfan Li, Yiran Wang, Yu Cheng +1
Policy optimization methods are powerful algorithms in Reinforcement Learning (RL) for their flexibility to deal with policy parameterization and ability to handle model misspecifi…
cs.LG2021★ 15 cited
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Youcai Zhang, Yuhao Cheng, Xinyu Huang +4
Multi-label learning in the presence of missing labels (MLML) is a challenging problem. Existing methods mainly focus on the design of network structures or training schemes, which…
cs.LG2021★ 3 cited
Deep Co-Attention Network for Multi-View Subspace Learning
Lecheng Zheng, Yu Cheng, Hongxia Yang +2
Many real-world applications involve data from multiple modalities and thus exhibit the view heterogeneity. For example, user modeling on social media might leverage both the topol…