Defensive Boosting for Online Probabilistic Forecasting
We study online probabilistic forecasting of binary outcomes chosen by an adaptive adversary. Given an online learning algorithm for a weak hypothesis class $H$, we would like to efficiently obtain two incomparable guarantees that existing online boosting techniques provide separately. Online gradient boosting competes in Brier score with the best predictor induced by the span of $H$ on every sequence, but promises nothing when the span does not contain an accurate predictor. Online weak-to-strong boosting drives classification error to zero under a weak-learning condition, but promises little when that condition fails. We give a simple defensive forecasting algorithm, the Defensive Boost...
| Authors | Georgy Noarov, Aaron Roth |
|---|---|
| Primary category | cs.LG |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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