We present a general approach to automating ethical decisions, drawing on machine
learning and computational social choice. In a nutshell, we propose to learn a model of …

We consider data in the form of pairwise comparisons of n items, with the goal of identifying
the top k items for some value of k< n, or alternatively, recovering a ranking of all the items …

This paper is concerned with the problem of top-K ranking from pairwise comparisons. Given
a collection of n items and a few pairwise comparisons across them, one wishes to identify …

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 …

Active ranking from pairwise comparisons and when parametric assumptions do not help Page
1 The Annals of Statistics 2019, Vol. 47, No. 6, 3099–3126 https://doi.org/10.1214/18-AOS1772 …

We consider the problem of preference bandits in the contextual setting. At each round, the
learner is presented with a context set of $ K $ items, chosen randomly from a potentially …

This paper studies the problem of rank aggregation under the Plackett-Luce model. The goal
is to infer a global ranking and related scores of the items, based on partial rankings …

We consider the classical problem of establishing a statistical ranking of a set of n items
given a set of inconsistent and incomplete pairwise comparisons between such items …

Group decision making is an important, long‐standing, and ubiquitous problem in all
societies, where collective decisions must be made by a group of agents despite individual …