{"type":"ScholarlyArticle","title":"Graph Space Embedding","authors":[{"name":"João Pereira","slug":"joao-pereira","given":"João","family":"Pereira","affiliation":"Amsterdam University Medical Center , The Netherlands; Horaizon BV , The Netherlands"},{"name":"Albert K. Groen","slug":"albert-k-groen","given":"Albert K.","family":"Groen","affiliation":"Amsterdam University Medical Center , The Netherlands"},{"name":"Erik S. G. Stroes","slug":"erik-s-g-stroes","given":"Erik S. G.","family":"Stroes","affiliation":"Amsterdam University Medical Center , The Netherlands"},{"name":"Evgeni Levin","slug":"evgeni-levin","given":"Evgeni","family":"Levin","affiliation":"Amsterdam University Medical Center , The Netherlands; Horaizon BV , The Netherlands"}],"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.","doi":"10.24963/ijcai.2019/451","url":"https://ijcai.org/proceedings/2019/451","pdf":"https://ijcai.org/proceedings/2019/0451.pdf","markdown":"https://ijcai.org/proceedings/2019/451.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/451","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":451,"pages":{"first":3253,"last":3259},"keywords":["Machine Learning: Interpretability","Machine Learning: Kernel Methods"],"videos":[],"references":[{"position":1,"raw":"[Assarsson et al., 2014] Erika Assarsson, Martin Lundberg, et al. 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