  The dueling bandits problem is an online learning framework for learning from
pairwise preference feedback, and is particularly well-suited for modeling
settings that elicit subjective or implicit human feedback. In this paper, we
study the problem of multi-dueling bandits with dependent arms, which extends
the original dueling bandits setting by simultaneously dueling multiple arms as
well as modeling dependencies between arms. These extensions capture key
characteristics found in many real-world applications, and allow for the
opportunity to develop significantly more efficient algorithms than were
possible in the original setting. We propose the \selfsparring algorithm, which
reduces the multi-dueling bandits problem to a conventional bandit setting that
can be solved using a stochastic bandit algorithm such as Thompson Sampling,
and can naturally model dependencies using a Gaussian process prior. We present
a no-regret analysis for multi-dueling setting, and demonstrate the
effectiveness of our algorithm empirically on a wide range of simulation
settings.
