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SUMMARY:Gaussian Processes to Study the Joint Dynamics of Networks and Tim
 e Series
DTSTART:20160622T083000
DTEND:20160622T103000
DTSTAMP:20260610T113928Z
UID:8e458203eb403db97890dbc5f7b136b455d9527d565534e3711c1fdf
CATEGORIES:Conferences - Seminars
DESCRIPTION:Victor Kristof\nEDIC Candidacy Exam\nExam President: Prof. Wul
 fram Gerstner\nThesis Director: Prof. Patrick Thiran\nThesis Co-director: 
 Prof. Matthias Grossglauser\nCo-examiner: Prof. Pascal Frossard\nBackgroun
 d papersRecurrent Gaussian Processes\, by C. L. C. Mattos et al.COEVOLVE: 
 A Joint Point Process Model for Information Diffusion and Network Co-evolu
 tion\, by M.Farajtabar et al.Spatial and Spatio-Temporal Log-Gaussian Cox 
 Processes: Extending the Geostatistical Paradigm\, by P. J. Diggle et al.A
 bstract\nGaussian processes are a class of statistical models whose popula
 rity in Machine Learning has been increasing dramatically in recent years.
  They provide a powerful framework for regression and classification\, whi
 le exhibiting great flexibility and interpretability. We describe in this 
 work how they can be extended into a recurrent structure to learn dynamica
 l patterns and how they can model spatio-temporal point processes. This wi
 ll lead us to the study of co-evolutionary network and time series\, and h
 ow Gaussian processes can be exploited to improve the existing joint dynam
 ics models.
LOCATION:BC 129 https://plan.epfl.ch/?room==BC%20129
STATUS:CONFIRMED
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