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linear bandits

topic1 events
papersTODAY 04:00 UTC

Minimax-Optimal Regret Bounds for Linear Contextual Bandits with Adaptive Action Sets

A new arXiv paper studies stochastic linear contextual bandits where the set of available actions can vary arbitrarily, depending on both the unknown parameter and past interactions. The authors prove matching upper and lower bounds on regret that agree up to logarithmic factors, characterizing the problem's minimax rate.

bandits/reinforcement-learning/learning-theory/arxiv