Bandits

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Bandits

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Multi-Armed Bandits

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Generalized Linear Bandit

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Optimal Bandit Algorithms

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Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
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Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity
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Spectral bandits for smooth graph functions with applications in recommender systems
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May 17, 2026Taesan Kim, Hyeongjun Yun, Jaegul Choo +1Linear Contextual Bandits Bandits
Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects
May 17, 2026Xi Chen, Shibo Dai, Jiameng Lyu +1Generalized Linear Bandit Semiparametric Framework
On the Power of Adaptivity for $\varepsilon$ -Best Arm Identification in Linear Bandits
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Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits
May 14, 2026Donghao Li, Chengshuai Shi, Weijuan Ou +2Prompt Engineering Generalized Linear Bandit
The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
May 13, 2026Maximilian Graf, Victor ThuotChange-Point Detection Optimal Sample Complexity
Collaborating in Multi-Armed Bandits with Strategic Agents
May 13, 2026Idan Barnea, Ofir Schlisselberg, Yishay MansourMulti-Armed Bandits Strategic Agents

Jun 17, 2026 · cs.LGJ/K move · Enter open · S save

Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits

Lennert Saerens, Bram Silue, Eleni Litsa, Peter Vrancx+1

Artificial Intelligence Lab, Departement of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium · imec, Leuven, Belgium

Identifying Pareto optimal solutions is critical to support multi-objective decision-making. We introduce the first anytime Multi-Objective Multi-Armed Bandit algorithm for the Pareto Set Identification problem, taking a Bayesian approach: Top-Two Pareto Front Thompson Sampling (TTPFTS). We benchmark TTPFTS against state-of-the-art fixed-budget Pareto Set Identification algorithms on synthetic environments. Next, we demonstrate its practical utility in a challenging multi-objective molecular discovery setting by efficiently exploring an ultra-large synthesis-on-demand molecular library. Furthermore, we introduce a novel uncertainty quantification metric that estimates our algorithm's confidence in the predicted Pareto set. We demonstrate that this metric effectively proxies true performance, yielding a robust methodology for monitoring learning progress in complex settings. Finally, we complement these empirical findings with a theoretical proof of the algorithm's asymptotic correctness.

Optimal Bandit Algorithms Thompson Sampling Pareto Frontier Bayesian

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