Learning Choice Functions via Pareto-Embeddings

• URL: http://arxiv.org/abs/2007.06927v1
• Date: Tue, 14 Jul 2020 09:34:44 GMT
• Title: Learning Choice Functions via Pareto-Embeddings
• Authors: Karlson Pfannschmidt, Eyke H\"ullermeier
• Abstract summary: We consider the problem of learning to choose from a given set of objects, where each object is represented by a feature vector. We propose a learning algorithm that minimizes a differentiable loss function suitable for this task.
• Score: 3.1410342959104725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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