Anomaly detection in time-series has a wide range of practical applications. While numerous
anomaly detection methods have been proposed in the literature, a recent survey concluded …

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 …

The Mallows model is a popular distribution for ranked data. We empirically and theoretically
analyze how the properties of rankings sampled from the Mallows model change when …

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 …

We consider a preference learning setting where every participant chooses an ordered list of
$ k $ most preferred items among a displayed set of candidates.(The set can be different for …

The concept of median/consensus has been widely investigated in order to provide a
statistical summary of ranking data, ie realizations of a random permutation $\Sigma $ of a …

Broad topics in online platforms represent a type of meso-scale between individual user-
defined communities and the whole platform; they typically consist of related communities …

We consider the problem of recovering the ground truth ordering (ranking, top-$ k $, or
others) over a large number of alternatives. The wisdom of crowd is a heuristic approach …

Collective decision making problems capture situations where the preferences of agents
need to be aggregated into a compromise solution. This thesis focuses on two such …

This research introduces a new ranking scheme capable of ordering competing units in a
more continual manner. The classic ranks are functionalized by incorporating data range …