# Utility-based Dueling Bandits as a Partial Monitoring Game

arXiv: https://arxiv.org/abs/1507.02750 · HTML: https://mlsanity.com/paper/1507.02750/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2015.

## Built on by (most cited first, 4 of 4)

- [Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications](https://mlsanity.com/paper/2302.03683/) (2023)
- [A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of Theta(T2/3) and its Application to Best-of-Both-Worlds](https://mlsanity.com/paper/2405.20028/) (2024)
- [Adversarial Multi-dueling Bandits](https://mlsanity.com/paper/2406.12475/) (2024)
- [Instance-Dependent Regret Bounds for Nonstochastic Linear Partial Monitoring](https://mlsanity.com/paper/2510.19158/) (2025)

## Abstract (arXiv)

Partial monitoring is a generic framework for sequential decision-making with incomplete feedback. It encompasses a wide class of problems such as dueling bandits, learning with expect advice, dynamic pricing, dark pools, and label efficient prediction. We study the utility-based dueling bandit problem as an instance of partial monitoring problem and prove that it fits the time-regret partial monitoring hierarchy as an easy - i.e. Theta (sqrt{T})- instance. We survey some partial monitoring algorithms and see how they could be used to solve dueling bandits efficiently. Keywords: Online learning, Dueling Bandits, Partial Monitoring, Partial Feedback, Multiarmed Bandits