Graph space embedding

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3 Citations (Scopus)

Abstract

We propose the Graph Space Embedding (GSE), a technique that maps the input into a space where interactions are implicitly encoded, with little computations required. We provide theoretical results on an optimal regime for the GSE, namely a feasibility region for its parameters, and demonstrate the experimental relevance of our findings. Next, we introduce a strategy to gain insight on which interactions are responsible for the certain predictions, paving the way for a far more transparent model. In an empirical evaluation on a real-world clinical cohort containing patients with suspected coronary artery disease, the GSE achieves far better performance than traditional algorithms.
Original languageEnglish
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
EditorsSarit Kraus
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3253-3259
Volume2019-August
ISBN (Electronic)9780999241141
Publication statusPublished - 2019
Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, Switzerland
Duration: 10 Aug 201916 Aug 2019

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence

Conference

Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Country/TerritorySwitzerland
CityMacao
Period10/08/201916/08/2019

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