Evolving-Graph Gaussian Processes

David Blanco-Mulero; Markus Heinonen; Ville Kyrki

Evolving-Graph Gaussian Processes

David Blanco-Mulero^*, Markus Heinonen, Ville Kyrki

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Konferenssiesitys › Scientific › vertaisarvioitu

65 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Sivumäärä	6
Tila	Julkaistu - heinäk. 2021
OKM-julkaisutyyppi	Ei sovellu
Tapahtuma	International Conference on Machine Learning: Time Series Workshop - Virtual, Online Kesto: 24 heinäk. 2021 → 24 heinäk. 2021 http://roseyu.com/time-series-workshop/ https://roseyu.com/time-series-workshop/

Workshop

Workshop	International Conference on Machine Learning: Time Series Workshop
Lyhennettä	TSW-ICML
Kaupunki	Virtual, Online
Ajanjakso	24/07/2021 → 24/07/2021
www-osoite	http://roseyu.com/time-series-workshop/ https://roseyu.com/time-series-workshop/

Pääsy asiakirjaan

TSW-ICML2021_paper_21Accepted author manuscript, 2 MB

Muut tiedostot ja linkit

Github repository

-: AI hämähäkin seitti
Kyrki, V. (Vastuullinen tutkija), Arndt, K. (Projektin jäsen), Petrik, V. (Projektin jäsen) & Blanco Mulero, D. (Projektin jäsen)
01/01/2018 → 31/12/2022
Projekti: Academy of Finland: Other research funding

Siteeraa tätä

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title = "Evolving-Graph Gaussian Processes",

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 assessthe 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.",

keywords = "gaussian process, Time series, Graph-based learning, probabilistic models",

author = "David Blanco-Mulero and Markus Heinonen and Ville Kyrki",

year = "2021",

month = jul,

language = "English",

note = "International Conference on Machine Learning: Time Series Workshop, TSW-ICML ; Conference date: 24-07-2021 Through 24-07-2021",

url = "http://roseyu.com/time-series-workshop/, https://roseyu.com/time-series-workshop/",

}

TY - CONF

T1 - Evolving-Graph Gaussian Processes

AU - Blanco-Mulero, David

AU - Heinonen, Markus

AU - Kyrki, Ville

PY - 2021/7

Y1 - 2021/7

N2 - 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 assessthe 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.

AB - 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 assessthe 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.

KW - gaussian process

KW - Time series

KW - Graph-based learning

KW - probabilistic models

UR - https://github.com/dblanm/evolving-ggp

M3 - Paper

T2 - International Conference on Machine Learning: Time Series Workshop

Y2 - 24 July 2021 through 24 July 2021

ER -

Evolving-Graph Gaussian Processes

Abstrakti

Workshop

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

-: AI hämähäkin seitti

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