  The aggregation of k-ary preferences is a historical and important problem,
since it has many real-world applications, such as peer grading, presidential
elections and restaurant ranking. Meanwhile, variants of Plackett-Luce model
has been applied to aggregate k-ary preferences. However, there are two urgent
issues still existing in the current variants. First, most of them ignore
feature information. Namely, they consider k-ary preferences instead of
instance-dependent k-ary preferences. Second, these variants barely consider
the uncertainty in k-ary preferences provided by agnostic crowds. In this
paper, we propose Deep plAckeTt-luce modEL wIth uNcertainty mEasurements
(DATELINE), which can address both issues simultaneously. To address the first
issue, we employ deep neural networks mapping each instance into its ranking
score in Plackett-Luce model. Then, we present a weighted Plackett-Luce model
to solve the second issue, where the weight is a dynamic uncertainty vector
measuring the worker quality. More importantly, we provide theoretical
guarantees for DATELINE to justify its robustness.
