  We introduce the problem of regret minimization in adversarial multi-dueling
bandits. While adversarial preferences have been studied in dueling bandits,
they have not been explored in multi-dueling bandits. In this setting, the
learner is required to select $m \geq 2$ arms at each round and observes as
feedback the identity of the most preferred arm which is based on an arbitrary
preference matrix chosen obliviously. We introduce a novel algorithm, MiDEX
(Multi Dueling EXP3), to learn from such preference feedback that is assumed to
be generated from a pairwise-subset choice model. We prove that the expected
cumulative $T$-round regret of MiDEX compared to a Borda-winner from a set of
$K$ arms is upper bounded by $O((K \log K)^{1/3} T^{2/3})$. Moreover, we prove
a lower bound of $\Omega(K^{1/3} T^{2/3})$ for the expected regret in this
setting which demonstrates that our proposed algorithm is near-optimal.
