Calibeating for general proper losses: A Bregman divergence approach
This work introduces a general framework for calibeating based on regret minimization. As compared to Foster and Hart's seminal calibeating work which had specialized treatments of Brier score (squared loss) and log loss, we consider a large family of proper losses that includes $α$-Tsallis losses (for $α\in [1, 2]$) and Lipschitz losses. Our results for Tsallis losses also hold for an unscaled version of Tsallis loss that recovers log loss. Our analysis is oriented around the Bregman divergence view of a proper loss. Technically, our results for the family of Tsallis losses that we consider are U-calibration results, simultaneously obtaining logarithmic regret for all losses in this fami...
| Authors | Maximilian Fichtl, Cristóbal Guzmán, Nishant A. Mehta |
|---|---|
| Primary category | cs.LG |
| Published | 17 May 2026 |
| Last updated at source | 30 June 2026 |
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