Evolving-Graph Gaussian Processes
| dc.contributor | Aalto-yliopisto | fi |
| dc.contributor | Aalto University | en |
| dc.contributor.author | Blanco-Mulero, David | en_US |
| dc.contributor.author | Heinonen, Markus | en_US |
| dc.contributor.author | Kyrki, Ville | en_US |
| dc.contributor.department | Department of Electrical Engineering and Automation | en |
| dc.contributor.department | Department of Computer Science | en |
| dc.contributor.groupauthor | Intelligent Robotics | en |
| dc.contributor.groupauthor | Probabilistic Machine Learning | en |
| dc.contributor.groupauthor | Professorship Kaski Samuel | en |
| dc.contributor.groupauthor | Professorship Lähdesmäki Harri | en |
| dc.date.accessioned | 2021-08-25T06:51:19Z | |
| dc.date.available | 2021-08-25T06:51:19Z | |
| dc.date.issued | 2021-07 | en_US |
| dc.description.abstract | Graph Gaussian Processes (GGPs) provide a dataefficient solution on graph structured domains. Existing approaches have focused on static structures, whereas many real graph data represent a dynamic structure, limiting the applications of GGPs. To overcome this we propose evolvingGraph Gaussian Processes (e-GGPs). The proposed method is capable of learning the transition function of graph vertices over time with a neighbourhood kernel to model the connectivity and interaction changes between vertices. We assess the performance of our method on time-series regression problems where graphs evolve over time. We demonstrate the benefits of e-GGPs over static graph Gaussian Process approaches. | en |
| dc.description.version | Peer reviewed | en |
| dc.format.extent | 6 | |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Blanco-Mulero, D, Heinonen, M & Kyrki, V 2021, ' Evolving-Graph Gaussian Processes ', Paper presented at International Conference on Machine Learning: Time Series Workshop, Virtual, Online, 24/07/2021 - 24/07/2021 . < https://arxiv.org/abs/2106.15127 > | en |
| dc.identifier.other | PURE UUID: 3f2dfaad-dce8-49db-9d8b-e1fb09189533 | en_US |
| dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/3f2dfaad-dce8-49db-9d8b-e1fb09189533 | en_US |
| dc.identifier.other | PURE LINK: https://github.com/dblanm/evolving-ggp | en_US |
| dc.identifier.other | PURE LINK: https://arxiv.org/abs/2106.15127 | en_US |
| dc.identifier.other | PURE LINK: http://roseyu.com/time-series-workshop/submissions/2021/TSW-ICML2021_paper_21.pdf | en_US |
| dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/66231149/TSW_ICML2021_paper_21.pdf | en_US |
| dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/109123 | |
| dc.identifier.urn | URN:NBN:fi:aalto-202108258360 | |
| dc.language.iso | en | en |
| dc.relation.ispartof | International Conference on Machine Learning | en |
| dc.rights | openAccess | en |
| dc.subject.keyword | gaussian process | en_US |
| dc.subject.keyword | Time series | en_US |
| dc.subject.keyword | Graph-based learning | en_US |
| dc.subject.keyword | probabilistic models | en_US |
| dc.title | Evolving-Graph Gaussian Processes | en |
| dc.type | A4 Artikkeli konferenssijulkaisussa | fi |
| dc.type.version | acceptedVersion |
