16 citations · 19 across the 7 of their papers we have counts for
8 papers
Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation
Li-Rong Zhou, Qin-Wen Luo, Sheng-Jun Huang
Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset, but its performance is fundamentally limited by dataset coverage. Action preference que…
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…
Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach
Chen-Chen Zong, Sheng-Jun Huang
Active learning (AL), which iteratively queries the most informative examples from a large pool of unlabeled candidates for model training, faces significant challenges in the pres…
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…
Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning
Heng-Bo Fan, Ming-Kun Xie, Jia-Hao Xiao +1
Due to the lack of extensive precisely-annotated multi-label data in real word, semi-supervised multi-label learning (SSMLL) has gradually gained attention. Abundant knowledge embe…
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…