不依赖传统方法，用新框架提升非平稳数据预测可靠性。
Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors
- 用嵌套预测集定义更广的置信预测器，替代传统共形预测器
- 在线场景下计算更快，批量场景下无需校准集，效率更高
- 适合数据有限或非平稳环境下的不确定性量化任务
自适应共形推断（ACI）在非交换性数据下提供有限样本覆盖保证，提升预测可靠性。本研究证明，这些优良性质并不依赖于共形预测器（CP）。只要满足嵌套预测集这一关键特性，更广泛的置信预测器即可保证相同理论保障。我们实证对比了非共形置信预测器（NCCP）与CP在非交换数据上结合ACI的表现。在线设置中，NCCP显著降低计算开销，同时保持相当的预测效率；在批量设置中，归纳型NCCP（INCCP）可利用全部训练数据，无需单独校准集，从而在数据稀缺时表现优于归纳型共形预测器（ICP），提升整体效率。尽管初步结果表明NCCP是ACI下不确定性量化的一种理论严谨且实用的替代方案，仍需在更多数据集和预测器上开展进一步验证。
原文摘要 · Abstract (English)
Adaptive Conformal Inference (ACI) provides finite-sample coverage guarantees, enhancing the prediction reliability under non-exchangeability. This study demonstrates that these desirable properties of ACI do not require the use of Conformal Predictors (CP). We show that the guarantees hold for the broader class of confidence predictors, defined by the requirement of producing nested prediction sets, a property we argue is essential for meaningful confidence statements. We empirically investigate the performance of Non-Conformal Confidence Predictors (NCCP) against CP when used with ACI on non-exchangeable data. In online settings, the NCCP offers significant computational advantages while maintaining a comparable predictive efficiency. In batch settings, inductive NCCP (INCCP) can outperform inductive CP (ICP) by utilising the full training dataset without requiring a separate calibration set, leading to improved efficiency, particularly when the data are limited. Although these initial results highlight NCCP as a theoretically sound and practically effective alternative to CP for uncertainty quantification with ACI in non-exchangeable scenarios, further empirical studies are warranted across diverse datasets and predictors.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。