mlsanityData to 7 Oct 2026

Copeland Dueling Bandits

arXiv 1506.00312 ↗First posted 1 Jun 2015cs.LGcited by 36

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Abstract (arXiv)

A version of the dueling bandit problem is addressed in which a Condorcet winner may not exist. Two algorithms are proposed that instead seek to minimize regret with respect to the Copeland winner, which, unlike the Condorcet winner, is guaranteed to exist. The first, Copeland Confidence Bound (CCB), is designed for small numbers of arms, while the second, Scalable Copeland Bandits (SCB), works better for large-scale problems. We provide theoretical results bounding the regret accumulated by CCB and SCB, both substantially improving existing results. Such existing results either offer bounds of the form O(K log T) but require restrictive assumptions, or offer bounds of the form O(K2 log T) without requiring such assumptions. Our results offer the best of both worlds: O(K log T) bounds without restrictive assumptions.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2015.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 8; 2023: 5; 2024: 9; 2025: 2; 2026: 6.

36 papers.

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  1. 2021Dueling RL: Reinforcement Learning with Trajectory Preferencescited by 105
  2. 2021Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizabilitycited by 44
  3. 2024Feel-Good Thompson Sampling for Contextual Dueling Banditscited by 23
  4. 2023Variance-Aware Regret Bounds for Stochastic Contextual Dueling Banditscited by 22
  5. 2023Borda Regret Minimization for Generalized Linear Dueling Banditscited by 17
  6. 2022Batched Dueling Banditscited by 10
  7. 2022Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisonscited by 10
  8. 2021Preference learning along multiple criteria: A game-theoretic perspectivecited by 9
  9. 2021Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Banditscited by 9
  10. 2024Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedbackcited by 9
  11. 2022ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Banditscited by 8
  12. 2021Dueling Bandits with Adversarial Sleepingcited by 5
24 more
  1. 2022Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferencescited by 5
  2. 2022Dueling Convex Optimization with General Preferencescited by 5
  3. 2023When Can We Track Significant Preference Shifts in Dueling Bandits?cited by 5
  4. 2022Dueling Bandits: From Two-dueling to Multi-duelingcited by 4
  5. 2022One Arrow, Two Kills: An Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Banditscited by 3
  6. 2023Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resourcescited by 3
  7. 2024Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimizationcited by 3
  8. 2024Biased Dueling Bandits with Stochastic Delayed Feedbackcited by 3
  9. 2022An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problemcited by 2
  10. 2024Non-Stationary Dueling Bandits Under a Weighted Borda Criterioncited by 2
  11. 2024Multi-Player Approaches for Dueling Banditscited by 2
  12. 2025FraPPE: Fast and Efficient Preference-based Pure Explorationcited by 2
  13. 2025Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Optionscited by 2
  14. 2026Multi-Armed Bandits With Best-Action Queriescited by 2
  15. 2024DP-Dueling: Learning from Preference Feedback without Compromising User Privacycited by 1
  16. 2024Adversarial Multi-dueling Banditscited by 1
  17. 2024QuACK: A Multipurpose Queuing Algorithm for Cooperative k-Armed Banditscited by 1
  18. 2026Active Evaluation of General Agents: Problem Definition and Comparison of Baseline Algorithmscited by 1
  19. 2021Statistical Consequences of Dueling Banditscited by 0
  20. 2023Identifying Copeland Winners in Dueling Bandits with Indifferencescited by 0
  21. 2026Best-of-Both-Worlds Multi-Dueling Bandits: Unified Algorithms for Stochastic and Adversarial Preferences under Condorcet and Borda Objectivescited by 0
  22. 2026A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedbackcited by 0
  23. 2026Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfarecited by 0
  24. 2026Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Banditscited by 0