BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Computational Optimisation Group - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Computational Optimisation Group
X-ORIGINAL-URL:http://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20110101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20121030T140000
DTEND;TZID=UTC:20121030T140000
DTSTAMP:20260505T075334
CREATED:20170124T102146Z
LAST-MODIFIED:20170124T102146Z
UID:595-1351605600-1351605600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Multi-task Learning and Matrix Regularization
DESCRIPTION:Title: Multi-task Learning and Matrix RegularizationSpeaker: Prof. Massimiliano PontilAffiliation: Department of Computer Science – University College LondonLocation: Room 301 William PenneyTime: 2:00pm \nAbstract. We discuss the problem of estimating a structured matrix with a large number of elements. A key motivation for this problem occurs in multi-task learning. In this case\, the columns of the matrix correspond to the parameters of different regression or classification tasks\, and there is structure due to relations between the tasks. We present a general method to learn the tasks’ parameters as well as their structure. Our approach is based on solving a convex optimization problem\, involving a data term and a penalty term. We highlight different types of penalty terms which are of practical and theoretical importance. They implement structural relations between the tasks and achieve a sparse representations of parameters. We address computational issues as well as the predictive performance of the method. Finally\, we describe some recent applications of these methods to computer vision and human computer interaction. \nAbout the speaker. Massimiliano Pontil is Professor of Computational Statistics and Machine Learning in the Department of Computer Science at University College London. His research interests are in the field of machine learning with a focus on regularization methods\, convex optimization and statistical estimation. He has published about 100 research papers on these topics\, is regularly in the programme committee of the leading conferences in the field\, is an associate editor of the Machine Learning Journal and is a member of the scientific advisory board of the Max Planck Institute for Biological Cybernetics\, Germany.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-multi-task-learning-and-matrix-regularization/
END:VEVENT
END:VCALENDAR