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
models). These models arise when we have a heterogeneous population of voters formed
by two populations, one of which is a subpopulation of expert voters. We show the
identifiability of both components and the learnability of their respective parameters. These
results are based upon, first, bounding the sample complexity for the Borda algorithm with …

In this paper, we consider mixtures of two Mallows models for top-$ k $ rankings, both with
the same location parameter but with different scale parameters, ie, a mixture of concentric
Mallows models. This situation arises when we have a heterogeneous population of voters
formed by two homogeneous populations, one of which is a subpopulation of expert voters
while the other includes the non-expert voters. We propose efficient sampling algorithms for
Mallows top-$ k $ rankings. We show the identifiability of both components, and the …