用e值拓展置信预测的边界，实现更灵活的不确定性量化。
E-Values Expand the Scope of Conformal Prediction
- 以e值替代传统p值，构建新型置信预测框架
- 支持批量任意时间验证、自适应覆盖率等新能力
- 适合需要强可靠性保证的高风险决策场景
置信预测是一种强大的分布无关不确定性量化框架。标准方法依赖于预测得分的排名：在可交换性假设下，未来测试点的排名不会过于极端。该方法可重新表述为p值形式。本文探索基于e值的替代方法，即置信e预测。e值具有p值无法实现的关键优势，使新的理论和实践能力成为可能。具体展示三个应用：批量任意时间有效的置信预测、数据依赖覆盖率的固定大小预测集，以及不确定真实标签下的置信预测。这些例子表明，基于e值的构造为置信预测工具箱提供了灵活扩展。
原文摘要 · Abstract (English)
Conformal prediction is a powerful framework for distribution-free uncertainty quantification. The standard approach to conformal prediction relies on comparing the ranks of prediction scores: under exchangeability, the rank of a future test point cannot be too extreme relative to a calibration set. This rank-based method can be reformulated in terms of p-values. In this paper, we explore an alternative approach based on e-values, known as conformal e-prediction. E-values offer key advantages that cannot be achieved with p-values, enabling new theoretical and practical capabilities. In particular, we present three applications that leverage the unique strengths of e-values: batch anytime-valid conformal prediction, fixed-size conformal sets with data-dependent coverage, and conformal prediction under ambiguous ground truth. Overall, these examples demonstrate that e-value-based constructions provide a flexible expansion of the toolbox of conformal prediction.
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