提出新校准策略，让在线选择性预测更可靠
Online Selective Conformal Prediction: Errors and Solutions
- 设计新校准数据选择方法，保持数据交换性
- 确保选择后预测区间覆盖率达标，错误率可控
- 适合需要高可靠性预测的实时系统
在在线选择性共形推断中，数据按顺序到达，仅当满足在线选择规则时才构建预测区间。由于在线选择可能破坏选定测试样本与其余数据之间的可交换性，必须通过适当选择校准数据来纠正。本文评估了现有校准选择策略，指出其关于选择条件覆盖率和假覆盖率（FCR）控制的声称存在根本性错误。为解决这些问题，我们提出新的校准选择策略，可证明地保持校准数据与选定测试样本的交换性。由此证明，采用这些策略的在线选择性共形推断能同时保证选择条件覆盖率和FCR控制。理论结果得到实验验证，展示了不同有效方法间的权衡。
原文摘要 · Abstract (English)
In online selective conformal inference, data arrives sequentially, and prediction intervals are constructed only when an online selection rule is met. Since online selections may break the exchangeability between the selected test datum and the rest of the data, one must correct for this by suitably selecting the calibration data. In this paper, we evaluate existing calibration selection strategies and pinpoint some fundamental errors in the associated claims that guarantee selection-conditional coverage and control of the false coverage rate (FCR). To address these shortcomings, we propose novel calibration selection strategies that provably preserve the exchangeability of the calibration data and the selected test datum. Consequently, we demonstrate that online selective conformal inference with these strategies guarantees both selection-conditional coverage and FCR control. Our theoretical findings are supported by experimental evidence examining tradeoffs between valid methods.
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