Finite-sample certification of coverage regret for budgeted document selection under adaptive stratified auditing
Selecting a budget-constrained set of documentary fragments, for instance the knowledge base of a retrieval-augmented generation system, raises a statistical question: how much expected query coverage is lost relative to the best feasible selection when the query distribution is unknown? We construct finite-sample, distribution-free upper confidence bounds for this coverage regret that remain valid after data-dependent selection. A pilot stage may select the document set, screen competitors, build comparison networks and learn an annotation policy. Then an independent audit acquires relevance labels with probabilities that are constant within observable strata. The main result is a simult...
| Authors | Nora Serdyukova |
|---|---|
| Primary category | math.ST |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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