用纠错方式预测，让模型在任何情况下都表现稳定。
In Defense of Defensive Forecasting
- 通过修正历史错误来生成预测，而非依赖预判。
- 在在线学习、校准等任务中达到近最优性能。
- 适合对鲁棒性要求高的实际应用，如金融与医疗预测。
本教程综述了防御性预测算法，其核心思想是通过纠正过去错误而非依赖预判来生成预测。该方法由Vovk提出，将预测目标建模为一个序列博弈，旨在无论实际结果如何都能最小化损失指标。本文介绍了这一理论的入门知识，并推导出适用于在线学习、校准、专家建议与在线置信预测的简单且近最优的算法。
原文摘要 · Abstract (English)
This tutorial provides a survey of algorithms for Defensive Forecasting, where predictions are derived not by prognostication but by correcting past mistakes. Pioneered by Vovk, Defensive Forecasting frames the goal of prediction as a sequential game, and derives predictions to minimize metrics no matter what outcomes occur. We present an elementary introduction to this general theory and derive simple, near-optimal algorithms for online learning, calibration, prediction with expert advice, and online conformal prediction.
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