Ranking-and-Selection with Multiple Correct Answers and Non-Answerable Estimates
Qiaoqiao Wang, Wei You
cs.LG, stat.ML
Submitted: 2026-06-20
Comments: Accepted for oral presentation at the 2026 Winter Simulation Conference
License: http://creativecommons.org/licenses/by/4.0/
The gist: We study fixed-precision ranking-and-selection in structured settings where the answer may be non-unique and where noisy estimates may temporarily admit no valid answer at all.
Terminology
Abstract
We study fixed-precision ranking-and-selection in structured settings where the answer may be non-unique and where noisy estimates may temporarily admit no valid answer at all. This phenomenon arises naturally in problems such as multi-fidelity ranking-and-selection and identifying a Condorcet winner from pairwise comparisons. To address this, we propose a unified framework based on answer-wise acceptance sets, restricted generalized likelihood ratio stopping, and an answer-pitfall decomposition that yields a max-max-min characteristic value and a common sampling principle. We introduce ENDS, a general procedure that combines estimation, nomination, pitfall detection, and cost-aware information-directed selection. We instantiate ENDS for various problems by deriving explicit formulas. Extensive numerical experiments show that this unified recipe performs well across a broad range of pure-exploration problems and offers a practical framework and proof-of-concept algorithmic recipe.
Sources
- Gradient Ascent for Active Exploration in Bandit Problems
- Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification
- Admissible anytime-valid sequential inference must rely on nonnegative martingales
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