mlsanityData to 14 Oct 2026

Multi-objective Bandits: Optimizing the Generalized Gini Index

arXiv 1706.04933 ↗First posted 15 Jun 2017cs.LGcited by 28

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Abstract (arXiv)

We study the multi-armed bandit (MAB) problem where the agent receives a vectorial feedback that encodes many possibly competing objectives to be optimized. The goal of the agent is to find a policy, which can optimize these objectives simultaneously in a fair way. This multi-objective online optimization problem is formalized by using the Generalized Gini Index (GGI) aggregation function. We propose an online gradient descent algorithm which exploits the convexity of the GGI aggregation function, and controls the exploration in a careful way achieving a distribution-free regret tildebigO (T-1/2 ) with high probability. We test our algorithm on synthetic data as well as on an electric battery control problem where the goal is to trade off the use of the different cells of a battery in order to balance their respective degradation rates.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jun 2017.

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Citing papers per year: 2022: 3; 2023: 4; 2024: 4; 2025: 5; 2026: 12.

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  1. 2023Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferencescited by 26
  2. 2025Sequential Learning of the Pareto Front for Multi-objective Banditscited by 15
  3. 2022Optimizing generalized Gini indices for fairness in rankingscited by 13
  4. 2022Pareto Regret Analyses in Multi-objective Multi-armed Banditcited by 13
  5. 2023Ad-load Balancing via Off-policy Learning in a Content Marketplacecited by 12
  6. 2024Multi-Objective Recommendation via Multivariate Policy Learningcited by 10
  7. 2023Fairness in Preference-based Reinforcement Learningcited by 9
  8. 2024Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphscited by 9
  9. 2024No-Regret Learning for Fair Multi-Agent Social Welfare Optimizationcited by 8
  10. 2023Optimal Scalarizations for Sublinear Hypervolume Regretcited by 7
  11. 2025Provably Efficient Multi-Objective Bandit Algorithms under Preference-Centric Customizationcited by 5
  12. 2025Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcarecited by 3
16 more
  1. 2022Contextual bandits with concave rewards, and an application to fair rankingcited by 2
  2. 2026Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learningcited by 2
  3. 2026Efficient Online Lexicographic Generalized Low-Rank Matrix Banditscited by 2
  4. 2025Multi-thresholding Good Arm Identification with Bandit Feedbackcited by 1
  5. 2025Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithmcited by 1
  6. 2026BanditLP: Large-Scale Stochastic Optimization for Personalized Recommendationscited by 1
  7. 2026Multi-Objective Multi-Agent Bandits: From Learning Efficiency to Fairness Optimizationcited by 1
  8. 2024Online Pareto-Optimal Decision-Making for Complex Tasks using Active Inferencecited by 0
  9. 2026Procedural Fairness in Multi-Agent Banditscited by 0
  10. 2026Are Stochastic Multi-objective Bandits Harder than Single-objective Bandits?cited by 0
  11. 2026Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfarecited by 0
  12. 2026Selective Ensemble Based on Preference-Directed Multi-Objective Banditscited by 0
  13. 2026Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systemscited by 0
  14. 2026Top-k Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selectioncited by 0
  15. 2026Inference-Time Policy Alignment for Fair Reinforcement Learningcited by 0
  16. 2026When More Objectives Make Identification Easier: The Role of Preferences and Feedbackcited by 0