7 citations · 13 across the 5 of their papers we have counts for
5 papers
Bidirectional Uncertainty-Based Active Learning for Open Set Annotation
Chen-Chen Zong, Ye-Wen Wang, Kun-Peng Ning +2
Active learning (AL) in open set scenarios presents a novel challenge of identifying the most valuable examples in an unlabeled data pool that comprises data from both known and un…
Active Learning for Open-set Annotation
Kun-Peng Ning, Xun Zhao, Yu Li +1
Existing active learning studies typically work in the closed-set setting by assuming that all data examples to be labeled are drawn from known classes. However, in real annotation…
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries
Kun-Peng Ning, Lue Tao, Songcan Chen +1
In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improvi…
Co-Imitation Learning without Expert Demonstration
Kun-Peng Ning, Hu Xu, Kun Zhu +1
Imitation learning is a primary approach to improve the efficiency of reinforcement learning by exploiting the expert demonstrations. However, in many real scenarios, obtaining exp…
Reinforcement Learning with Supervision from Noisy Demonstrations
Kun-Peng Ning, Sheng-Jun Huang
Reinforcement learning has achieved great success in various applications. To learn an effective policy for the agent, it usually requires a huge amount of data by interacting with…