  We consider the problem of learning to choose from a given set of objects,
where each object is represented by a feature vector. Traditional approaches in
choice modelling are mainly based on learning a latent, real-valued utility
function, thereby inducing a linear order on choice alternatives. While this
approach is suitable for discrete (top-1) choices, it is not straightforward
how to use it for subset choices. Instead of mapping choice alternatives to the
real number line, we propose to embed them into a higher-dimensional utility
space, in which we identify choice sets with Pareto-optimal points. To this
end, we propose a learning algorithm that minimizes a differentiable loss
function suitable for this task. We demonstrate the feasibility of learning a
Pareto-embedding on a suite of benchmark datasets.
