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cs.LG2026
Bringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning
Yu Chen, Weijun Lv, Yue Huang +2
Label noise in multi-label learning (MLL) poses significant challenges for model training, particularly in partial multi-label learning (PML) where candidate labels contain both re…
cs.LG2026
Feature-Label Modal Alignment for Robust Partial Multi-Label Learning
Yu Chen, Weijun Lv, Yue Huang +4
In partial multi-label learning (PML), each instance is associated with a set of candidate labels containing both ground-truth and noisy labels. The presence of noisy labels disrup…
cs.LG2021★ 3 cited
GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning
Jiajun Fan, Changnan Xiao, Yue Huang
Deep Q Network (DQN) firstly kicked the door of deep reinforcement learning (DRL) via combining deep learning (DL) with reinforcement learning (RL), which has noticed that the dist…