Learning Choice Functions via Pareto-Embeddings

Pfannschmidt, Karlson und Hüllermeier, Eyke (2020): Learning Choice Functions via Pareto-Embeddings. 43rd German Conference on AI, Bamberg, Germany, 21-25 September, 2020. In: KI 2020: Advances in Artificial Intelligence, Bd. 12325 Cham: Springer. S. 327-333

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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Publikationsform: Publisher's Version
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
• ISSN: 0302-9743
• Ort: Cham
• Sprache: Englisch