2 papers
cs.LG2026
Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries
Linfeng Cao, Ming Shi, Ness B. Shroff
Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives. Since arm utility depends on both unknown rewards and…
cs.LG2025
Provably Efficient Multi-Objective Bandit Algorithms under Preference-Centric Customization
Linfeng Cao, Ming Shi, Ness B. Shroff
Multi-objective multi-armed bandit (MO-MAB) problems traditionally aim to achieve Pareto optimality. However, real-world scenarios often involve users with varying preferences acro…