Large Language Models (LLMs) have unlocked new capabilities and applications; however,
evaluating the alignment with human preferences still poses significant challenges. To …

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural
networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging …

Ranking problems, also known as preference learning problems, define a widely spread
class of statistical learning problems with many applications, including fraud detection …

This paper explores the preference-based top-K rank aggregation problem. Suppose that a
collection of items is repeatedly compared in pairs, and one wishes to recover a consistent …

In machine learning, the notion of multi-armed bandits refers to a class of online learning
problems, in which an agent is supposed to simultaneously explore and exploit a given set …

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 …

Learning preference distributions is a critical problem in many areas (eg, recommender
systems, IR, social choice). However, many existing learning and inference methods impose …

We study the problem of online rank elicitation, assuming that rankings of a set of
alternatives obey the Plackett-Luce distribution. Following the setting of the dueling bandits …

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 …