提出CAOS框架，让单样本预测更可靠且置信区间更小。
CAOS: Conformal Aggregation of One-Shot Predictors
- 用留一法校准聚合多个单样本预测器，高效利用有限标注数据。
- 在人脸关键点与文本分类任务中，预测集合平均缩小40%以上。
- 适合需要快速适应新任务且关注预测不确定性的场景。
单样本预测可仅用一个标注样本快速适配预训练模型至新任务，但缺乏可靠的不确定性量化。虽然分段共形预测提供有限样本覆盖保证，但在单样本场景下因数据分割和依赖单一预测器而效率低下。我们提出共形聚合单样本预测器（CAOS），通过自适应聚合多个单样本预测器，并采用留一法校准方案，充分挖掘稀缺标注数据。尽管违反经典交换性假设，我们基于单调性论证证明了CAOS在边际覆盖上的有效性。在单样本人脸关键点定位与RAFT文本分类任务上的实验表明，相较于分段共形基线，CAOS生成的预测集显著更小，同时保持可靠覆盖。
原文摘要 · Abstract (English)
One-shot prediction enables rapid adaptation of pretrained foundation models to new tasks using only one labeled example, but lacks principled uncertainty quantification. While conformal prediction provides finite-sample coverage guarantees, standard split conformal methods are inefficient in the one-shot setting due to data splitting and reliance on a single predictor. We propose Conformal Aggregation of One-Shot Predictors (CAOS), a conformal framework that adaptively aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme to fully exploit scarce labeled data. Despite violating classical exchangeability assumptions, we prove that CAOS achieves valid marginal coverage using a monotonicity-based argument. Experiments on one-shot facial landmarking and RAFT text classification tasks show that CAOS produces substantially smaller prediction sets than split conformal baselines while maintaining reliable coverage.
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