We consider the problem of minimizing regret in the setting of advice-efficient multiarmed bandits with expert advice. We give an algorithm for the setting of $K$ arms and $N$ experts out of which we are allowed to query and use only $M$ experts’ advice in each round, which has a regret bound of $\tilde{O}\left(\sqrt{\frac{\min\{K, M\} N}{M} T}\right)$ after $T$ rounds. We also prove that any algorithm for this problem must have expected regret at least $\tilde{\Omega}\left(\sqrt{\frac{\min\{K, M\} N}{M}T}\right)$, thus showing that our upper bound is nearly tight. This solves the COLT 2013 open problem of Seldin et al.