Toward Simultaneously Optimal Regret in U-Calibration
U-calibration studies online forecasting algorithms whose predictions can be consumed by any unknown downstream agent, guaranteeing sublinear regret simultaneously for all proper loss functions. Existing U-calibration algorithms achieve worst-case optimal $O(\sqrt{T})$ regret for every bounded proper loss, but they fail to adapt to easier losses: as we show, even for smooth losses such as squared loss, they incur $Ω(\sqrt{T})$ regret instead of the optimal $O(\log T)$ regret. In this work, we show that this limitation is not inherent. Specifically, we design a single forecast algorithm that simultaneously achieves $\tilde O(\sqrt{T})$ regret for every bounded proper loss and $O(\log T)$ r...
| Authors | Rafael Frongillo, Haipeng Luo, Nishant A. Mehta et al. |
|---|---|
| Primary category | stat.ML |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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