BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.2.3.1//EN
BEGIN:VEVENT
UID:675@lincs.fr
DTSTART;TZID=Europe/Paris:20220209T143000
DTEND;TZID=Europe/Paris:20220209T173000
DTSTAMP:20220201T095020Z
URL:https://www.lincs.fr/events/graph-based-contributions-to-machine-learn
 ing/
SUMMARY:Graph-based contributions to machine learning
DESCRIPTION:A graph is a mathematical object that makes it possible to
 represent relationships (called edges) between entities (called nodes).
 Graphs have long been a focal point in a number of problems ranging from
 work by Euler to PageRank and shortest-path problems. In more recent
 times\, graphs have been used for machine learning.\n\nWith the advent of
 social networks and the world-wide web\, more and more datasets can be
 represented using graphs. Those graphs are ever bigger\, sometimes with
 billions of edges and billions of nodes. Designing efficient algorithms for
 analyzing those datasets has thus proven necessary. This thesis reviews the
 state of the art and introduces new algorithms for the clustering and the
 embedding of the nodes of massive graphs. Furthermore\, in order to
 facilitate the handling of large graphs and to apply the techniques under
 study\, we introduce Scikit-network\, a free and open-source Python library
 which was developed during the thesis. Many tasks\, such as the
 classification or the ranking of the nodes using centrality measures\, can
 be carried out thanks to Scikit-network.\n\nWe also tackle the problem of
 labeling data. Supervised machine learning techniques require labeled data
 to be trained. The quality of this labeled data has a heavy influence on
 the quality of the predictions of those techniques once trained. However\,
 building this data cannot be achieved through the sole use of machines and
 requires human intervention. We study the data labeling problem in a
 graph-based setting\, and we aim at describing the solutions that require
 as little human intervention as possible. We characterize those solutions
 and illustrate how they can be applied in real use-cases.
CATEGORIES:PhD Defense,Seminars
LOCATION:LINCS Seminars room\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.828400;2.356897
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=23\, avenue d'Italie\,
 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS Seminars
 room:geo:48.828400,2.356897
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:STANDARD
DTSTART:20211031T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
END:STANDARD
END:VTIMEZONE
END:VCALENDAR