Unverified50% confidenceFactExact time
The maximum information gain γ_T characterizes the upper limit on the information about the unknown function that an algorithm can obtain through observations in T rounds of interaction, and largely determines the difficulty boundary of the kernelized bandit problem.
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10/9/2026
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UnverifiedThis universal lower bound delineates the boundary for kernel bandit algorithm design: the regret performance of any new algorithm cannot surpass Ω(√(Tγ_T/log T)).70% similarUnverifiedThe d·log p sample complexity is the advantageous order possessed by previous sharp methods that relied on subset search, and HOST achieves this level without sacrificing polynomial runtime.65% similarUnverifiedThe lower bound for the standard multi-armed bandit problem is O(√T), and ORUCB's extra log(T+1) factor stems from the complexity introduced by simultaneously learning the correction function.64% similarUnverifiedWhen prior knowledge about possible fault patterns is available, selecting a sparse penalty structure that matches them often yields significant performance gains over general-purpose algorithms63% similarUnverifiedOffline reinforcement learning penalizes agents for deviating from the dataset distribution by introducing constraint algorithms such as CQL and IQL, but at the cost of limited exploration capability.63% similar
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