The objective of ordinal embedding is to find a Euclidean representation of a set of abstract
items, using only answers to triplet comparisons of the form" Is item i closer to item j or item …

We study the problem of interactively learning a binary classifier using noisy labeling and
pairwise comparison oracles, where the comparison oracle answers which one in the given …

Assume we are given a set of items from a general metric space, but we neither have access
to the representation of the data nor to the distances between data points. Instead, suppose …

We consider machine learning in a comparison-based setting where we are given a set of
points in a metric space, but we have no access to the actual distances between the points …

In this work, we propose a multi-objective decision making framework that accommodates
different user preferences over objectives, where preferences are learned via policy …

Coalitional stability in hedonic games has usually been considered in the setting where
agent preferences are fully known. We consider the setting where agent preferences are …

Submodular functions are discrete functions that model laws of diminishing returns and
enjoy numerous algorithmic applications. They have been used in many areas, including …

In this article, we address the problem of measuring and analyzing sensation, the subjective
magnitude of one's experience. We do this in the context of the method of triads: The …

We study PAC learnability and PAC stabilizability of Hedonic Games (HGs), ie, efficiently
inferring preferences or core-stable partitions from samples. We first expand the known …

There is increasing interest in learning algorithms that involve interaction between hu-man
and machine. Comparison-based queries are among the most natural ways to get feed-back …