  We consider the problem of top-k subset selection in Dueling Bandit problems
with score information. Real-world pairwise ranking problems often exhibit a
high degree of transitivity and prior work has suggested sampling methods that
exploit such transitivity through the use of parametric preference models like
the Bradley-Terry-Luce (BTL) and Thurstone models. To date, this work has
focused on cases where sample outcomes are win/loss binary responses. We extend
this to selection problems where sampling results contain quantitative
information by proposing a Thurstonian style model and adapting the Pairwise
Optimal Computing Budget Allocation for subset selection (POCBAm) sampling
method to exploit this model for efficient sample selection. We compare the
empirical performance against standard POCBAm and other competing algorithms.
