We study the problem of $ K $-armed dueling bandit for both stochastic and adversarial
environments, where the goal of the learner is to aggregate information through relative …

We consider the problem of estimating a ranking on a set of items from noisy pairwise
comparisons given item features. We address the fact that pairwise comparison data often …

We study the problem of $ K $-armed dueling bandit for both stochastic and adversarial
environments, where the goal of the learner is to aggregate information through relative …

We consider a ranking regression problem in which we use a dataset of ranked choices to
learn Plackett-Luce scores as functions of sample features. We solve the maximum …

We consider spectral approaches to the problem of ranking n players given their incomplete
and noisy pairwise comparisons, but revisit this classical problem in light of player covariate …

Metric elicitation is a recent framework for eliciting classification performance metrics that
best reflect implicit user preferences based on the task and context. However, available …

We consider the problem of ranking a set of $ n $ items given a sample of their pairwise
preferences. It is well known from the classical results of sorting literature that without any …

This paper addresses the challenges of aligning large language models (LLMs) with human
values via preference learning (PL), with a focus on the issues of incomplete and corrupted …

We consider a rank regression setting, in which a dataset of N samples with features in R d
is ranked by an oracle via M pairwise comparisons. Specifically, there exists a latent total …

We consider the problem of ranking a set of items from pairwise comparisons in the
presence of features associated with the items. Recent works have established that $ O …