用改进的上下文学习实现高效精准的不确定性预测。
Optimizing In-Context Learning for Efficient Full Conformal Prediction
- 设计可置换不变的Transformer模型，结合CP感知损失优化上下文学习。
- 无需重训练即可模拟全置信预测，覆盖率保持且计算开销大幅降低。
- 适合需要高可靠性和低计算成本的场景，如医疗或金融决策系统。
可靠的不确定性量化对可信人工智能至关重要。置信预测（CP）提供无需分布假设的覆盖保证，但其两种主要变体存在互补性局限：分割置信预测（SCP）因数据划分导致效率低下，而全置信预测（FCP）虽提升数据效率，却需高昂的重训练成本。基于元学习或上下文学习（ICL）的近期方法部分缓解了这些问题，但其训练过程未针对CP优化，常导致过大的预测集。本文提出高效全置信预测框架E-ICL+FCP，采用基于排列不变Transformer的ICL模型，并使用CP感知损失进行训练。该方法通过模拟FCP所需的多重重训练模型，无需实际重训练即可保持覆盖率，显著降低效率与计算开销。在合成与真实任务上的实验表明，相比现有SCP与FCP基线，E-ICL+FCP实现了更优的效率-覆盖率权衡。
原文摘要 · Abstract (English)
Reliable uncertainty quantification is critical for trustworthy AI. Conformal Prediction (CP) provides prediction sets with distribution-free coverage guarantees, but its two main variants face complementary limitations. Split CP (SCP) suffers from data inefficiency due to dataset partitioning, while full CP (FCP) improves data efficiency at the cost of prohibitive retraining complexity. Recent approaches based on meta-learning or in-context learning (ICL) partially mitigate these drawbacks. However, they rely on training procedures not specifically tailored to CP, which may yield large prediction sets. We introduce an efficient FCP framework, termed enhanced ICL-based FCP (E-ICL+FCP), which employs a permutation-invariant Transformer-based ICL model trained with a CP-aware loss. By simulating the multiple retrained models required by FCP without actual retraining, E-ICL+FCP preserves coverage while markedly reducing both inefficiency and computational overhead. Experiments on synthetic and real tasks demonstrate that E-ICL+FCP attains superior efficiency-coverage trade-offs compared to existing SCP and FCP baselines.
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