Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret
stat.ML, cs.LG
Submitted: 2026-10-06
Updated: 2026-10-06
Terminology
Sources
- Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
- Fair Algorithms for Infinite and Contextual Bandits
- Revisiting Social Welfare in Bandits: UCB is (Nearly) All You Need
- Improved Algorithms for Nash Welfare in Linear Bandits
- No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
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