Calibeating on Theoretical Online Learning Setting
2Asymptotic BoundDagan et al. [2025]
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| Dagan et al. [2025]Outcome assumption=none2026.05 | 2 | — | — | — | |
| Theorem 3.1Outcome assumption=none2026.05 | 1 | — | — | 2 | |
| Theorem F.2Outcome assumption=none2026.05 | 1 | — | — | 1 | |
| Dagan et al. [2025]Outcome assumption=none2026.05 | 0.54 | — | — | — | |
| Algorithm 3Loss class=Brier - K-class2026.03 | — | -1 | -1 | — | |
| Foster and HartLoss class=Brier - binary2026.03 | — | 2 | 2 | — | |
| Foster and HartLoss class=Brier - K-class2026.03 | — | 2 | 2 | — | |
| Theorem A.10Outcome assumption=i.i.d.2026.05 | — | — | — | 1 |