提出自适应近邻方法，让模型在标签不完整时仍能准确学习。
Learnability with Partial Labels and Adaptive Nearest Neighbors
- 设计自适应近邻算法，自动调整邻居数量以应对不完整标签
- 在多种场景下优于现有方法，理论性能有保证
- 适合标签获取成本高、标注不全的现实任务
部分标签学习（PLL）研究发现，即使每个样本仅关联一组标签而非单一精确标签，学习依然可行。然而，部分标签学习的可行条件尚不明确，现有方法仅在特定场景有效。本文从数学上刻画了PLL可行的设置，并提出一种通用的自适应近邻算法PL A-$k$NN，该方法在一般场景下表现优异，且具备强性能保障。实验表明，PL A-$k$NN在典型部分标签场景中超越当前最优方法。
原文摘要 · Abstract (English)
Prior work on partial labels learning (PLL) has shown that learning is possible even when each instance is associated with a bag of labels, rather than a single accurate but costly label. However, the necessary conditions for learning with partial labels remain unclear, and existing PLL methods are effective only in specific scenarios. In this work, we mathematically characterize the settings in which PLL is feasible. In addition, we present PL A-$k$NN, an adaptive nearest-neighbors algorithm for PLL that is effective in general scenarios and enjoys strong performance guarantees. Experimental results corroborate that PL A-$k$NN can outperform state-of-the-art methods in general PLL scenarios.
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