In Defense of Defensive Forecasting

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.

@article{perdomo2025_2506.11848,
  title={ In Defense of Defensive Forecasting },
  author={ Juan Carlos Perdomo and Benjamin Recht },
  journal={arXiv preprint arXiv:2506.11848},
  year={ 2025 }
}