提出可随时验证的不确定性控制方法，确保预测集在任意时刻都有效。
Anytime-Valid Conformal Risk Control
- 基于分位数构造动态校准框架，实现任意时间点的误差控制
- 在分布漂移场景中仍保持高概率有效性，且渐近紧致
- 适合需要持续监控预测可靠性的实际应用，如医疗或金融
预测集为预测任务中的不确定性提供了量化手段。通过使用独立校准数据，共形预测与风险控制可在计算高效的前提下实现统计上有效的误差控制。然而，在标准设定中，误差仅在固定大小的校准数据集的多次重复中平均意义上受控。本文将控制扩展至累积增长的校准数据集，在任意时间点均以高概率保持有效性。我们基于分位数论证推导出此类保证，并在涉及分布漂移的场景中展示了所提框架的应用性。进一步建立了匹配的下界，证明了我们的保证在渐近意义下是紧致的。最后，通过模拟和真实数据实验验证了方法的实际性能。
原文摘要 · Abstract (English)
Prediction sets provide a means of quantifying the uncertainty in predictive tasks. Using held out calibration data, conformal prediction and risk control can produce prediction sets that exhibit statistically valid error control in a computationally efficient manner. However, in the standard formulations, the error is only controlled on average over many possible calibration datasets of fixed size. In this paper, we extend the control to remain valid with high probability over a cumulatively growing calibration dataset at any time point. We derive such guarantees using quantile-based arguments and illustrate the applicability of the proposed framework to settings involving distribution shift. We further establish a matching lower bound and show that our guarantees are asymptotically tight. Finally, we demonstrate the practical performance of our methods through both simulations and real-world numerical examples.
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。