Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

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

Abstract In Machine Learning, a benchmark refers to an ensemble of datasets associated
with one or multiple metrics together with a way to aggregate different systems …

Aggregating multiple rankings in a database is an important task well studied by the
database community. High-stakes application domains include hiring, lending, and …

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 …

Rank aggregation with pairwise comparisons has shown promising results in elections,
sports competitions, recommendations, and information retrieval. However, little attention …

In rank aggregation, members of a population rank issues to decide which are collectively
preferred. We focus instead on identifying divisive issues that express disagreements …

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 …

Label Ranking (LR) is the supervised task of learning a sorting function that maps feature
vectors $ x\in\mathbb {R}^ d $ to rankings $\sigma (x)\in\mathbb S_k $ over a finite set of $ k …

As pairwise ranking becomes broadly employed for elections, sports competitions,
recommendation, information retrieval and so on, attackers have strong motivation and …