Guo Zeng

A Novel General Framework for Sharp Lower Bounds in Succinct Stochastic Bandits

Many online learning applications adopt the stochastic bandit problem with a linear reward model, where the unknown parameter exhibits a succinct structure. We study minimax regret lower bounds which allow to know whether more efficient algorithms can be proposed. We introduce a general definition o…