16 citations · 19 across the 5 of their papers we have counts for
5 papers
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL
Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang +1
Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset. To alleviate extrapolation errors, existing studies often uniformly regularize the valu…
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL
Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang +1
Offline-to-online (O2O) reinforcement learning (RL) provides an effective means of leveraging an offline pre-trained policy as initialization to improve performance rapidly with li…
Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation
Ye-Wen Wang, Chen-Chen Zong, Ming-Kun Xie +1
Active learning (AL) has achieved great success by selecting the most valuable examples from unlabeled data. However, they usually deteriorate in real scenarios where open-set nois…
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…
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
Chen-Chen Zong, Ye-Wen Wang, Ming-Kun Xie +1
Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs). Existing approaches address this issue through loss correction or…