mlsanityData to 14 Oct 2026

Thresholding Bandit for Dose-ranging: The Impact of Monotonicity

arXiv 1711.04454 ↗First posted 13 Nov 2017math.ST stat.ML stat.THcited by 16

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Abstract (arXiv)

We analyze the sample complexity of the thresholding bandit problem, with and without the assumption that the mean values of the arms are increasing. In each case, we provide a lower bound valid for any risk δ and any δ-correct algorithm; in addition, we propose an algorithm whose sample complexity is of the same order of magnitude for small risks. This work is motivated by phase 1 clinical trials, a practically important setting where the arm means are increasing by nature, and where no satisfactory solution is available so far.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Nov 2017.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 3; 2024: 2; 2025: 4; 2026: 1.

16 papers.

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  1. 2021Best Arm Identification with Safety Constraintscited by 25
  2. 2025Sequential Learning of the Pareto Front for Multi-objective Banditscited by 15
  3. 2021SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groupscited by 6
  4. 2021Multi-armed Bandit Requiring Monotone Arm Sequencescited by 5
  5. 2021Problem Dependent View on Structured Thresholding Bandit Problemscited by 5
  6. 2021Online Sign Identification: Minimization of the Number of Errors in Thresholding Banditscited by 3
  7. 2022Measurement-based Admission Control in Sliced Networks: A Best Arm Identification Approachcited by 3
  8. 2024LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problemcited by 2
  9. 2023Pure Exploration under Mediators' Feedbackcited by 1
  10. 2025Fixed-Confidence Multiple Change Point Identification under Bandit Feedbackcited by 1
  11. 2026Non-Asymptotic Best Policy Identification Guarantees in Online Reinforcement Learningcited by 1
  12. 2023An Asymptotically Optimal Algorithm for the Convex Hull Membership Problemcited by 0
4 more
  1. 2023Markdown Pricing Under an Unknown Parametric Demand Modelcited by 0
  2. 2024Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraintscited by 0
  3. 2025Locally Differentially Private Thresholding Banditscited by 0
  4. 2025Threshold-Based Optimal Arm Selection in Monotonic Bandits: Regret Lower Bounds and Algorithmscited by 0