Clustering is the task of automatically gathering observations into homogeneous groups,
where the number of groups is unknown. Through its basis in a statistical modeling …

Learning from human feedback has shown to be a useful approach in acquiring robot
reward functions. However, expert feedback is often assumed to be drawn from an …

Ranking is one of the simple and efficient data collection techniques to understand
individuals' perception and preferences for some items such as products, people, and …

This paper presents the R package PlackettLuce, which implements a generalization of the
Plackett–Luce model for rankings data. The generalization accommodates both ties (of …

We consider the problem of subset selection where one is given multiple rankings of items
and the goal is to select the highest" quality" subset. Score functions from the multiwinner …

In the field of optimization and machine learning, the statistical assessment of results has
played a key role in conducting algorithmic performance comparisons. Classically, null …

In this paper, we study mixtures of two Mallows models for top-$ k $ rankings with equal
location parameters but with different scale parameters (a mixture of concentric Mallows …

In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much
popularity in the evolutionary computation community for solving optimization problems …

Rankings and scores are two common data types used by judges to express preferences
and/or perceptions of quality in a collection of objects. Numerous models exist to study data …

We use the" map of elections" approach of Szufa et al.(AAMAS 2020) to analyze several well-
known vote distributions. For each of them, we give an explicit formula or an efficient …