研究预测者只能在部分时间点预测时的最优误差边界。
Online Prediction with Limited Selectivity
- 限定预测起始时间点，分析最坏情况下的预测误差
- 提出复杂度度量，能精确给出每类实例的误差上限
- 适用于需在有限时机中做决策的在线预测场景
选择性预测模型允许预测者自由决定预测的时间窗口。许多数据统计可在无需分布假设或专家指导的情况下达到非平凡的预测误差，但这些结果依赖于预测者可在任意时刻开始预测。本文引入受限选择性预测（PLS）模型，其中预测者仅能在时间轴的子集上启动预测。我们研究了在逐实例和平均情况下的最优预测误差，并提出了一个复杂度度量，可对每个实例给出最优误差的紧致上界。对于随机生成的PLS实例，该上界以高概率成立。
原文摘要 · Abstract (English)
Selective prediction [Dru13, QV19] models the scenario where a forecaster freely decides on the prediction window that their forecast spans. Many data statistics can be predicted to a non-trivial error rate without any distributional assumptions or expert advice, yet these results rely on that the forecaster may predict at any time. We introduce a model of Prediction with Limited Selectivity (PLS) where the forecaster can start the prediction only on a subset of the time horizon. We study the optimal prediction error both on an instance-by-instance basis and via an average-case analysis. We introduce a complexity measure that gives instance-dependent bounds on the optimal error. For a randomly-generated PLS instance, these bounds match with high probability.
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