  In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$
stochastic bandit arms with unknown reward distributions. Our goal is to
identify the $K$ arms with the largest means with high confidence, by drawing
samples from the arms adaptively. This problem is motivated by various
practical applications and has attracted considerable attention in the past
decade. In this paper, we propose new practical algorithms for the Best-$K$-Arm
problem, which have nearly optimal sample complexity bounds (matching the lower
bound up to logarithmic factors) and outperform the state-of-the-art algorithms
for the Best-$K$-Arm problem (even for $K=1$) in practice.
