WE1.SC3.2
Learning Time-Varying Graphs for Heavy-Tailed Data Clustering
Amirhossein Javaheri, Daniel P. Palomar, Hong Kong University of Science and Technology, China
Session:
WE1.SC3: Graph Learning and Graph Signal Processing Lecture
Track:
TMTSP - Theoretical and Methodological Trends in Signal Processing
Location:
Saint Clair 3
Presentation Time:
Wed, 28 Aug, 10:50 - 11:10 France Time (UTC +1)
Session Chair:
Luiz Chamon, University of Stuttgart
Presentation
Discussion
Resources
No resources available.
Session WE1.SC3
WE1.SC3.1: Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation
Keivan Faghih Niresi, Lucas Kuhn, Gaëtan Frusque, Olga Fink, EPFL, Switzerland
WE1.SC3.2: Learning Time-Varying Graphs for Heavy-Tailed Data Clustering
Amirhossein Javaheri, Daniel P. Palomar, Hong Kong University of Science and Technology, China
WE1.SC3.3: JIPDA Filtering with Information Diffusion
Kamil Dedecius, Jan Novák, Petr Jechumtál, Czech Technical University in Prague, Czech Republic
WE1.SC3.4: False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
Taulant Koka, Jasin Machkour, Michael Muma, Technische Universität Darmstadt, Germany
WE1.SC3.5: Learning smooth graphs with sparse temporal variations to explore long-term financial trends
Cécile Bastidon, Université de Toulon, France; Myriam Bontonou, ENS de Lyon, France; Pierre Borgnat, Pablo Jensen, Patrice Abry, CNRS, ENS de Lyon, France; Antoine Parent, Université Paris 8, France
WE1.SC3.6: Leveraging Standardization in Graph Learning
Thu Ha Phi, University Paris Nanterre, France; Alexandre Hippert-Ferrer, Université Gustave Eiffel, France; Florent Bouchard, University Paris Saclay, France; Arnaud Breloy, Conservatoire National des Arts et Metiers, France