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cs.LG2024
Learning Causal Transition Matrix for Instance-dependent Label Noise
Jiahui Li, Tai-Wei Chang, Kun Kuang +3
Noisy labels are both inevitable and problematic in machine learning methods, as they negatively impact models' generalization ability by causing overfitting. In the context of lea…
cs.AI2024
Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions
Tianhao Ma, Han Chen, Juncheng Hu +2
Learning from label proportions (LLP), i.e., a challenging weakly-supervised learning task, aims to train a classifier by using bags of instances and the proportions of classes wit…