  We study a novel pure exploration problem: the $\epsilon$-Thresholding Bandit
Problem (TBP) with fixed confidence in stochastic linear bandits. We prove a
lower bound for the sample complexity and extend an algorithm designed for Best
Arm Identification in the linear case to TBP that is asymptotically optimal.
