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Optimal transport for graphs: definitions, applications to graph-signal processing

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Authors : Courty, Nicolas (Author of the conference)
CIRM (Publisher )

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Abstract : In this talk I will discuss how a variant of the classical optimal transport problem, known as the Gromov-Wasserstein distance, can help in designing learning tasks over graphs, and allow to transpose classical signal processing or data analysis tools such as dictionary learning or online change detection, for learning over those types of structured objects. Both theoretical and practical aspects will be discussed.

Keywords : transport optimal; graphs; Gromov-Wasserstein

MSC Codes :
68Q32 - Computational learning theory
68T05 - Learning and adaptive systems

Additional resources :
https://www.cirm-math.fr/RepOrga/2588/Slides/courty.pdf

    Information on the Video

    Film maker : Hennenfent, Guillaume
    Language : English
    Available date : 05/12/2022
    Conference Date : 10/11/2022
    Subseries : Research talks
    arXiv category : Machine Learning
    Mathematical Area(s) : Computer Science
    Format : MP4 (.mp4) - HD
    Video Time : 00:54:45
    Targeted Audience : Researchers ; Graduate Students ; Doctoral Students, Post-Doctoral Students
    Download : https://videos.cirm-math.fr/2022-11-10_Courty.mp4

Information on the Event

Event Title : Machine Learning and Signal Processing on Graphs / Apprentissage automatique et traitement du signal sur graphes
Event Organizers : Keriven, Nicolas ; Loukas, Andreas ; Pustelnik, Nelly ; Tremblay, Nicolas ; Vaiter, Samuel
Dates : 07/11/2022 - 11/11/2022
Event Year : 2022
Event URL : https://conferences.cirm-math.fr/2588.html

Citation Data

DOI : 10.24350/CIRM.V.19981603
Cite this video as: Courty, Nicolas (2022). Optimal transport for graphs: definitions, applications to graph-signal processing. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19981603
URI : http://dx.doi.org/10.24350/CIRM.V.19981603

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