Gaussian-Width Bounds for Offline-to-Online Linear Bandits
The authors investigate how partially covered offline data can improve online learning in linear bandits by addressing loose regret bounds from existing methods. They show that using concentration of Lipschitz functions of Gaussian vectors yields tighter bounds, leading to a phased elimination algorithm with a better regret order of $ ilde{O}(k au)$ instead of $ ilde{O}( au rac{ ext{something}}{d})$.