In Partial Label Learning (PLL), each instance is associated with a candidate label set, with exactly one label being true. While most studies implicitly assume balanced class distributions, real-world data often exhibit severe class imbalance distributions, leading to the Long-Tailed Partial Label Learning (LT-PLL) pr...
Xiangyu Ren, Mingxuan Xia, Guang-Cheng Zhu et al.· Proceedings of the Thirty-Fi...· 0 citations
Criterion-Distilled Policy Optimization (CriPO), which enhances rubric-based RL via on-policy self-distillation via on-policy self-distillation, to simultaneously address both unexplored and suppressed criteria.
Mingxuan Xia, Yuhang Yang, Chaonan Ye et al.· arXiv.org· 3 citations· ⚡2
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