Top- k Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection

arXiv:2607.26273 · cs.LG, cs.AI, stat.ML · Submitted 2026-07-28 · Read on arXiv

Nicolas Gutowski, Fabien Chhel, Alexandre Letard, Sylvain Lamprier

cs.LG, cs.AI, stat.ML

Submitted: 2026-07-28

Comments: 21 pages, 7 figures, 7 tables

Code: https://github.com/ngutowski/topk-pareto-bandits

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regret — 79× in this paper · explained in 4 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Reinforcement Learning — 11× in this paper · explained in 4 episodes
• Core — 8× in this paper · explained in 2 episodes
• Pruning — 7× in this paper · explained in 4 episodes
• Multi-objective Optimization — 5× in this paper · explained in 5 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Monotonicity — 4× in this paper · explained in 2 episodes
• Regret Bound — 4× in this paper · explained in 2 episodes
• Multi-Objective Multi-Armed Bandits — 3× in this paper
• Pareto Frontier — 3× in this paper · explained in 2 episodes
• Bayesian Optimization — 2× in this paper · explained in 3 episodes
• Parameterization — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 3 episodes
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 3 episodes
• Gap-Dependent Regret — 1× in this paper · explained in 2 episodes
• Human-in-the-loop — 1× in this paper · explained in 4 episodes
• Regret Bounds — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 6 episodes

Sources

• Provably Efficient Multi-Objective Bandit Algorithms under Preference-Centric Customization
• Best Group Identification in Multi-Objective Bandits
• Balancing Rewards in Text Summarization: Multi-Objective Reinforcement Learning via HyperVolume Optimization

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