# Multi-dueling Bandits with Dependent Arms

arXiv: https://arxiv.org/abs/1705.00253 · HTML: https://mlsanity.com/paper/1705.00253/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2017.

## Built on by (most cited first, 30 of 35)

- [A Minimaximalist Approach to Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2401.04056/) (2024)
- [Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability](https://mlsanity.com/paper/2111.12306/) (2021)
- [Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models](https://mlsanity.com/paper/2202.04593/) (2022)
- [Feel-Good Thompson Sampling for Contextual Dueling Bandits](https://mlsanity.com/paper/2404.06013/) (2024)
- [Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes](https://mlsanity.com/paper/2302.01513/) (2023)
- [Principled Preferential Bayesian Optimization](https://mlsanity.com/paper/2402.05367/) (2024)
- [Bias-Robust Bayesian Optimization via Dueling Bandits](https://mlsanity.com/paper/2105.11802/) (2021)
- [Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits](https://mlsanity.com/paper/2202.11795/) (2022)
- [Batched Dueling Bandits](https://mlsanity.com/paper/2202.10660/) (2022)
- [Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits](https://mlsanity.com/paper/2111.03917/) (2021)
- [ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits](https://mlsanity.com/paper/2210.14322/) (2022)
- [Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses](https://mlsanity.com/paper/2306.13104/) (2023)
- [LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization](https://mlsanity.com/paper/2510.13907/) (2025)
- [Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback](https://mlsanity.com/paper/2302.03805/) (2023)
- [Bandits with Preference Feedback: A Stackelberg Game Perspective](https://mlsanity.com/paper/2406.16745/) (2024)
- [Dueling Convex Optimization with General Preferences](https://mlsanity.com/paper/2210.02562/) (2022)
- [Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering](https://mlsanity.com/paper/2306.06779/) (2023)
- [Dueling Bandits with Adversarial Sleeping](https://mlsanity.com/paper/2107.02274/) (2021)
- [Efficient Exploration in Binary and Preferential Bayesian Optimization](https://mlsanity.com/paper/2110.09361/) (2021)
- [Dueling Bandits: From Two-dueling to Multi-dueling](https://mlsanity.com/paper/2211.10293/) (2022)
- [Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization](https://mlsanity.com/paper/2402.18917/) (2024)
- [Learning from a Learning User for Optimal Recommendations](https://mlsanity.com/paper/2202.01879/) (2022)
- [One Arrow, Two Kills: An Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits](https://mlsanity.com/paper/2210.14998/) (2022)
- [Combinatorial Bandits for Maximum Value Reward Function under Max Value-Index Feedback](https://mlsanity.com/paper/2305.16074/) (2023)
- [Faster Convergence with Multiway Preferences](https://mlsanity.com/paper/2312.11788/) (2023)
- [Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources](https://mlsanity.com/paper/2312.17229/) (2023)
- [Preferential Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2406.14699/) (2024)
- [On Regret Bounds of Thompson Sampling for Bayesian Optimization](https://mlsanity.com/paper/2603.09276/) (2026)
- [An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem](https://mlsanity.com/paper/2209.12108/) (2022)
- [Combinatorial Bandits without Total Order for Arms](https://mlsanity.com/paper/2103.02741/) (2021)

## Abstract (arXiv)

The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly well-suited for modeling settings that elicit subjective or implicit human feedback. In this paper, we study the problem of multi-dueling bandits with dependent arms, which extends the original dueling bandits setting by simultaneously dueling multiple arms as well as modeling dependencies between arms. These extensions capture key characteristics found in many real-world applications, and allow for the opportunity to develop significantly more efficient algorithms than were possible in the original setting. We propose the selfsparring algorithm, which reduces the multi-dueling bandits problem to a conventional bandit setting that can be solved using a stochastic bandit algorithm such as Thompson Sampling, and can naturally model dependencies using a Gaussian process prior. We present a no-regret analysis for multi-dueling setting, and demonstrate the effectiveness of our algorithm empirically on a wide range of simulation settings.