<ul data-eligibleForWebStory="true"><li>Researchers have developed a new algorithm, Col-UCB, to address the issue of imbalanced exploration in grouped multi-armed bandit problems.</li><li>In grouped bandit settings with overlapping feasible action sets, groups share reward observations to minimize collaborative regret, defined as the maximum regret across groups.</li><li>The objective of Col-UCB is to balance the exploration burden between groups or populations by dynamically coordinating exploration.</li><li>Col-UCB has been shown to achieve optimal minimax and instance-dependent collaborative regret with logarithmic factors.</li><li>The algorithm adapts to the structure of shared action sets between groups, offering insights into the benefits of collaboration over independent learning.</li></ul>

Collaborative Min-Max Regret in Grouped Multi-Armed Bandits

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