BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv6.3.0//EN
X-ORIGINAL-URL:http://computationalgenomics.bioinformatics.ucla.edu/
X-WR-CALNAME:CGSI
X-WR-CALDESC:Computational Genomics Summer Institute
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
DTSTART;TZID=America/Los_Angeles:20190730T133000
DTEND;TZID=America/Los_Angeles:20190730T141500
DTSTAMP:20190713T185600
UID:MEC-1a344877f11195aaf947ccfe48ee9c89@computationalgenomics.bioinformatics.ucla.edu
CREATED:20190713
LAST-MODIFIED:20190713
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:Yanjun Qi | Tutorial | Joint Learning of Multiple Related Gaussian Graphical Models from Heterogeneous Samples.
DESCRIPTION:Related papers:\nThe joint graphical lasso for inverse covariance estimation across multiple classes. Danaher P1, Wang P2, Witten DM1. J R Stat Soc Series B Stat Methodol. 2014 Mar;76(2):373-397.\nA Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models\nB Wang, A Sekhon, Y Qi, Proceedings of the 35th International Conference on Machine Learning, 5148-5157\nFast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure\nB Wang, A Sekhon, Y Qi\nProceedings of the 21st International Conference on Artificial Intelligence …\n
URL:http://computationalgenomics.bioinformatics.ucla.edu/events/yanjun-qi-tutorial-joint-learning-of-multiple-related-gaussian-graphical-models-from-heterogeneous-samples/
LOCATION:Mong Auditorium
Engineering VI, UCLA Main Campus 
END:VEVENT
END:VCALENDAR