mlsanityData to 7 Oct 2026

Review on Ranking and Selection: A New Perspective

arXiv 2008.00249 ↗First posted 1 Aug 2020math.OC stat.MEcited by 28

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Abstract (arXiv)

In this paper, we briefly review the development of ranking-and-selection (R&S) in the past 70 years, especially the theoretical achievements and practical applications in the last 20 years. Different from the frequentist and Bayesian classifications adopted by Kim and Nelson (2006b) and Chick (2006) in their review articles, we categorize existing R&S procedures into fixed-precision and fixed-budget procedures, as in Hunter and Nelson (2017). We show that these two categories of procedures essentially differ in the underlying methodological formulations, i.e., they are built on hypothesis testing and dynamic-programming, respectively. In light of this variation, we review in detail some well-known procedures in the literature and show how they fit into these two formulations. In addition, we discuss the use of R&S procedures in solving various practical problems and propose what we think are the important research questions in the field.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2020.

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Citing papers per year: 2021: 1; 2022: 8; 2023: 4; 2024: 5; 2025: 6; 2026: 4.

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  1. 2022Top Two Algorithms Revisitedcited by 34
  2. 2022Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangiancited by 29
  3. 2022Minimax Optimal Algorithms for Fixed-Budget Best Arm Identificationcited by 20
  4. 2021Rate-optimal Bayesian Simple Regret in Best Arm Identificationcited by 11
  5. 2022Non-Asymptotic Analysis of a UCB-based Top Two Algorithmcited by 11
  6. 2023Best Arm Identification with Fairness Constraints on Subpopulationscited by 8
  7. 2022SPRT-based Efficient Best Arm Identification in Stochastic Banditscited by 3
  8. 2023Worst-Case Optimal Multi-Armed Gaussian Best Arm Identification with a Fixed Budgetcited by 3
  9. 2024Collaborative Intelligence in Sequential Experiments: A Human-in-the-Loop Framework for Drug Discoverycited by 3
  10. 2023Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulationcited by 2
  11. 2024Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selectioncited by 2
  12. 2022Suboptimal Performance of the Bayes Optimal Algorithm in Frequentist Best Arm Identificationcited by 1
16 more
  1. 2022Choosing Answers in varepsilon-Best-Answer Identification for Linear Banditscited by 1
  2. 2024Identifying the Best Arm in the Presence of Global Environment Shiftscited by 1
  3. 2025Admissibility of Completely Randomized Trials: A Large-Deviation Approachcited by 1
  4. 2025SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluationscited by 1
  5. 2025TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problemscited by 1
  6. 2026Best-Arm Identification with Generative Proxycited by 1
  7. 2022Selecting the Best Optimizing Systemcited by 0
  8. 2023Efficient Learning for Selecting Top-m Context-Dependent Designscited by 0
  9. 2024Fixed Confidence Best Arm Identification in the Bayesian Settingcited by 0
  10. 2024New Additive OCBA Procedures for Robust Ranking and Selectioncited by 0
  11. 2025Regular Tree Search for Simulation Optimizationcited by 0
  12. 2025Sequential Additivity in Distributionally Robust Ranking and Selectioncited by 0
  13. 2025UCB for Large-Scale Pure Exploration: Beyond Sub-Gaussianitycited by 0
  14. 2026Depth over Fidelity in Fixed-Budget Noisy Evolution Strategiescited by 0
  15. 2026Annealed Entropic Allocation for Ranking and Selectioncited by 0
  16. 2026Ranking-and-Selection with Multiple Correct Answers and Non-Answerable Estimatescited by 0