Rank Confidence Sequences:Anytime-valid Leaderboards
Leaderboards rank models by their average scores on benchmark items, and they are consulted repeatedly while the evaluation is still running. Existing confidence intervals for a model's rank control their error rate only if they are computed once, after a number of items chosen in advance. If they are recomputed as results arrive, and the evaluation stops once they look decisive, their error rate exceeds its nominal level. Anytime-valid methods keep their guarantees at all sample sizes simultaneously and hence under any stopping rule. They exist for the accuracy of one model, for one pair of models and for the set of models that may be best. For pairwise battles they also give ranks. None...
| Authors | Hamed Khosravi, Xiaoming Huo |
|---|---|
| Primary category | stat.ME |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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