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DTSTART:20110101T000000
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DTSTART;TZID=UTC:20121127T140000
DTEND;TZID=UTC:20121127T140000
DTSTAMP:20260418T153226
CREATED:20170124T102145Z
LAST-MODIFIED:20170124T102145Z
UID:592-1354024800-1354024800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Matrix Learning Problems and First-Order Optimization
DESCRIPTION:Title: Matrix Learning Problems and First-Order OptimizationSpeaker: Dr. Andreas Argyriou Affiliation: Toyota Technological Institute at ChicagoLocation: Room 217 Huxley BuildingTime: 2:00pm \nAbstract. In the past few years\, there has been significant interest in nonsmooth convex optimization problems involving matrices\, especially in the areas of machine learning\, statistics and control. Instances of such problems are multitask learning and matrix completion\, robust PCA\, sparse inverse covariance selection etc. I will present PRISMA\, a new optimization algorithm for minimizing a convex objective which decomposes into three parts: a smooth part\, a simple non-smooth Lipschitz part\, and a simple nonsmooth non-Lipschitz part. Our algorithm combines the methodologies of smoothing and accelerated proximal methods. Moreover\, our convergence result removes the assumption of bounded domain required by Nesterov's smoothing methods. I will show how PRISMA can be applied to the problems of max-norm regularized matrix completion and clustering\, robust PCA and sparse inverse covariance selection\, and compare to state of the art methods.  \nAbout the speaker. Andreas Argyriou has received degrees in Computer Science from MIT and a PhD in Computer Science from UCL. The topic of his PhD work has been on machine learning methodologies integrating different tasks and data sources. He has held postdoctoral and research faculty positions at UCL\, TTI Chicago\, KU Leuven and is currently in Ecole Centrale Paris with an RBUCE-UP fellowship. His current interests are in the areas of machine learning with big and complex data\, compressed sensing and convex optimization methods.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-matrix-learning-problems-and-first-order-optimization/
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