mlsanityData to 7 Oct 2026

Regret Analysis for Continuous Dueling Bandit

arXiv 1711.07693 ↗First posted 21 Nov 2017stat.ML cs.LGcited by 21

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Abstract (arXiv)

The dueling bandit is a learning framework wherein the feedback information in the learning process is restricted to a noisy comparison between a pair of actions. In this research, we address a dueling bandit problem based on a cost function over a continuous space. We propose a stochastic mirror descent algorithm and show that the algorithm achieves an O(√Tlog T)-regret bound under strong convexity and smoothness assumptions for the cost function. Subsequently, we clarify the equivalence between regret minimization in dueling bandit and convex optimization for the cost function. Moreover, when considering a lower bound in convex optimization, our algorithm is shown to achieve the optimal convergence rate in convex optimization and the optimal regret in dueling bandit except for a logarithmic factor.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 6; 2025: 4; 2026: 3.

21 papers.

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  1. 2021Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizabilitycited by 44
  2. 2022Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Modelscited by 35
  3. 2023Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oraclescited by 34
  4. 2024Feel-Good Thompson Sampling for Contextual Dueling Banditscited by 23
  5. 2023Variance-Aware Regret Bounds for Stochastic Contextual Dueling Banditscited by 22
  6. 2024Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inferencecited by 12
  7. 2025ComPO: Preference Alignment via Comparison Oraclescited by 11
  8. 2024Bandits with Preference Feedback: A Stackelberg Game Perspectivecited by 7
  9. 2025LLM Routing with Dueling Feedbackcited by 6
  10. 2022Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferencescited by 5
  11. 2022Dueling Convex Optimization with General Preferencescited by 5
  12. 2023Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resourcescited by 3
9 more
  1. 2024Optimism in the Face of Ambiguity Principle for Multi-Armed Banditscited by 3
  2. 2021Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-Fidelity Feedbackcited by 2
  3. 2024Learning from Imperfect Human Feedback: a Tale from Corruption-Robust Duelingcited by 2
  4. 2024DP-Dueling: Learning from Preference Feedback without Compromising User Privacycited by 1
  5. 2026Lipschitz Dueling Bandits over Continuous Action Spacescited by 1
  6. 2026Online Convex Optimization with Dueling Feedbackcited by 1
  7. 2025Continuum-armed Bandit Optimization with Batch Pairwise Comparison Oraclescited by 0
  8. 2025Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithm on Stochastic Smooth Functionscited by 0
  9. 2026A Zeroth-Order Paradigm for LLM Preference Alignmentcited by 0