提出更优的随机性预测方法，比传统方法更可靠但计算更复杂。
Inductive randomness predictors: beyond conformal
- 基于随机性假设，构建比传统方法更强的预测框架
- 理论上最优预测性能提升最多约2.72倍（e倍）
- 适合对预测可靠性要求高的研究者深入探索
本文提出归纳随机性预测器，其构成归纳共形预测器的真超集，且在随机性假设（即独立同分布数据）下保持有效性。研究表明，所有非平凡的归纳共形预测器均被某个归纳随机性预测器严格支配，理论最优性能提升最多可达约2.72倍（e倍）。然而，这种改进通常较小，且新方法计算更复杂。因此，本文不建议用归纳随机性预测器替代现有方法，仅呼吁对其开展更深入研究。
原文摘要 · Abstract (English)
This paper introduces inductive randomness predictors, which form a proper superset of inductive conformal predictors but have the same principal property of validity under the assumption of randomness (i.e., of IID data). It turns out that every non-trivial inductive conformal predictor is strictly dominated by an inductive randomness predictor, although the improvement is not great, at most a factor of $\mathrm{e}\approx2.72$ in the case of e-prediction. The dominating inductive randomness predictors are more complicated and more difficult to compute; besides, an improvement by a factor of $\mathrm{e}$ is rare. Therefore, this paper does not suggest replacing inductive conformal predictors by inductive randomness predictors and only calls for a more detailed study of the latter.
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