  We present a modified tuning of the algorithm of Zimmert and Seldin [2020]
for adversarial multiarmed bandits with delayed feedback, which in addition to
the minimax optimal adversarial regret guarantee shown by Zimmert and Seldin
simultaneously achieves a near-optimal regret guarantee in the stochastic
setting with fixed delays. Specifically, the adversarial regret guarantee is
$\mathcal{O}(\sqrt{TK} + \sqrt{dT\log K})$, where $T$ is the time horizon, $K$
is the number of arms, and $d$ is the fixed delay, whereas the stochastic
regret guarantee is $\mathcal{O}\left(\sum_{i \neq i^*}(\frac{1}{\Delta_i}
\log(T) + \frac{d}{\Delta_{i}\log K}) + d K^{1/3}\log K\right)$, where
$\Delta_i$ are the suboptimality gaps. We also present an extension of the
algorithm to the case of arbitrary delays, which is based on an oracle
knowledge of the maximal delay $d_{max}$ and achieves $\mathcal{O}(\sqrt{TK} +
\sqrt{D\log K} + d_{max}K^{1/3} \log K)$ regret in the adversarial regime,
where $D$ is the total delay, and $\mathcal{O}\left(\sum_{i \neq
i^*}(\frac{1}{\Delta_i} \log(T) + \frac{\sigma_{max}}{\Delta_{i}\log K}) +
d_{max}K^{1/3}\log K\right)$ regret in the stochastic regime, where
$\sigma_{max}$ is the maximal number of outstanding observations. Finally, we
present a lower bound that matches regret upper bound achieved by the skipping
technique of Zimmert and Seldin [2020] in the adversarial setting.
