# Provably Efficient Multi-Objective Bandit Algorithms under Preference-Centric Customization

arXiv: https://arxiv.org/abs/2502.13457 · HTML: https://mlsanity.com/paper/2502.13457/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes](https://mlsanity.com/paper/2012.08507/) (2020)
- [Adversarial Attacks on Stochastic Bandits](https://mlsanity.com/paper/1810.12188/) (2018)
- [A Simple Approach for Non-stationary Linear Bandits](https://mlsanity.com/paper/2103.05324/) (2021)
- [Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions](https://mlsanity.com/paper/2205.06811/) (2022)
- [Multi-objective Bandits: Optimizing the Generalized Gini Index](https://mlsanity.com/paper/1706.04933/) (2017)
- [Multi-objective Contextual Bandit Problem with Similarity Information](https://mlsanity.com/paper/1803.04015/) (2018)
- [Multi-Objective Generalized Linear Bandits](https://mlsanity.com/paper/1905.12879/) (2019)
- [Pareto Regret Analyses in Multi-objective Multi-armed Bandit](https://mlsanity.com/paper/2212.00884/) (2022)
- [Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and Sensing](https://mlsanity.com/paper/2302.05257/) (2023)

## Built on by (most cited first, 5 of 5)

- [Communication-Corruption Coupling and Verification in Cooperative Multi-Objective Bandits](https://mlsanity.com/paper/2601.11924/) (2026)
- [Probe-then-Commit Multi-Objective Bandits: Theoretical Benefits of Limited Multi-Arm Feedback](https://mlsanity.com/paper/2602.03175/) (2026)
- [Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries](https://mlsanity.com/paper/2606.08410/) (2026)
- [Selective Ensemble Based on Preference-Directed Multi-Objective Bandits](https://mlsanity.com/paper/2606.21929/) (2026)
- [Top-k Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection](https://mlsanity.com/paper/2607.26273/) (2026)

## Abstract (arXiv)

Multi-objective multi-armed bandit (MO-MAB) problems traditionally aim to achieve Pareto optimality. However, real-world scenarios often involve users with varying preferences across objectives, resulting in a Pareto-optimal arm that may score high for one user but perform quite poorly for another. This highlights the need for customized learning, a factor often overlooked in prior research. To address this, we study a preference-aware MO-MAB framework in the presence of explicit user preference. It shifts the focus from achieving Pareto optimality to further optimizing within the Pareto front under preference-centric customization. To our knowledge, this is the first theoretical study of customized MO-MAB optimization with explicit user preferences. Motivated by practical applications, we explore two scenarios: unknown preference and hidden preference, each presenting unique challenges for algorithm design and analysis. At the core of our algorithms are preference estimation and preference-aware optimization mechanisms to adapt to user preferences effectively. We further develop novel analytical techniques to establish near-optimal regret of the proposed algorithms. Strong empirical performance confirm the effectiveness of our approach.