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DTSTART;VALUE=DATE:20170828
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SUMMARY:Autumn School: Optimization in Machine Learning and Data Science
DESCRIPTION:Large parts of the new economy rely on data science and the application of machine learning. For handling these modern computational and statistical problems\, optimization algorithms are of utmost importance. The typically large data sets involved in this context urge for new optimization techniques. \nTherefore\, we are delighted to announce Julian Hall\,  Tamara G. Kolda\, Stephen Wright as speakers at the upcoming ALOP Autumn School entitled “Optimization in Machine Learning and Data Science.” \nRepresenting three top class authorities in their respective fields of research\, reaching from optimization of signal and image processing over large scale linear programming problems to tensor decomposition and many more\, they will be sharing their experience and knowledge on theory and application of optimization algorithms. \nAutumn School 2017 Program (click for download) \nRegistration is closed \nInformation on scheduled Speakers:  \n  \nJulian Hall\, The University of Edinburgh –   \nHis research interests are the development of algorithmic and computational techniques for solving large scale linear programming (LP) problems using the revised simplex method on both serial and parallel computers. Consequential research interest is the application of these techniques in other areas of computation optimization and linear algebra. \nMr. Hall plans on talking about high performance simplex which will naturally lead into high performance numerical computation. He will look at computational issues in interior point methods and the applicability of “modern” first order methods to the solution of LP problems\, as well as talk about one of his current research topics. \n  \n Tamara G. Kolda\, Sandia National Laboratories –   \nHer research interests are computational algorithm design and development\, including linear and multi-linear algebra\, tensor decomposition\, tensor eigenvalues\, graph algorithms\, machine learning\, network science\, derivative-free optimization\, computational optimization\, distributed and parallel computing. \nMs. Kolda will talk about “Optimization Approaches for Fitting the Canonical Tensor Decomposition.” More specifically\, she will speak about \n\nthe theory and background of the CP decomposition\nstandard approaches such as alternating least squares and all-at-once optimization\nhandling missing data\npractical approaches to choosing the rank\nalternative objective functions for other statistical models/assumptions\nTime permitting\, she may also talk about HPC\, data structures\, parallelization\n\n  \nStephen J. Wright\, University of Wisconsin-Madison \n \nHis research interests are in continuous optimization algorithms and their applications. He works on devising algorithms for fundamental problems in continuous optimization (with practical relevance) and in understanding their theoretical properties. He also enjoys working with domain scientists and engineers in such areas as data analysis and machine learning\, machine learning\, process control\, and computer architecture to solve problems in these areas using formulation and solution techniques from optimization. \nMr. Wright will cover the background of fundamental algorithmic approaches that are relevant to data analysis problems. Topics will include the following: \n– first-order (gradient) methods for smooth unconstrained   minimization. \n    Steepest descent\, accelerated gradient and other momentum methods. \n– minimization of objectives with “simple” regularization terms using \n    forward-backward/prox-linear methods. \n– stochastic gradient and subgradient methods for minimizing convex objectives with “summation” form. \n– second-order methods for smooth (convex and nonconvex) objectives.  Newton’s method and methods that explicitly use \n   negative curvature information. \nWe will also give a survey of current use of optimization methodology in data analysis. \n  \nOrganizers:  \n  \nJan Pablo Burgard\, Ralf Münnich\, Ekkehard Sachs\, Volker Schulz\, Sven de Vries  \nSchedule\, Location and Travel Information:  \n  \nFeel free to download a copy of the schedule here:  Autumn School 2017 Program \nThe workshop is scheduled to begin on Monday\, August 28 at 9:00 am and end on Thursday\, August 31 in the afternoon.\n The workshop will take place in Building E located on Campus I at Trier University. \n For travel tips on how to get to Trier\, please check  here;\n Instructions on how to find Campus I are located here. \n \n  \nHotel Room Reservations \nWe have a reserved a hotel room contingent at the Altstadt Hotel\, Am Porta Nigra Platz\, 54292 Trier. Please contact them directly to make your reservations referencing the AUTUMN SCHOOL 2017. \nThe contingent will be available for reservation until August 1\, 2017\, or until all available rooms have been booked. \nRegistration information: \nThere will be a registration fee of 70 €. Detailed payment information will be communicated upon registration. \nQualified participants may be eligible for a stipend to assist with travel expenses. In order to be considered for the stipend\, please submit a CV (which should include the names and contact information of two professional references) along with a one-page letter of motivation before June 15\, 2017. \nRegistration is closed
URL:https://alop.uni-trier.de/event/autumn-school-optimization-in-machine-learning-and-data-science/
LOCATION:Trier University\, E Building\, Universitätsring 15\, Trier\, Germany
CATEGORIES:Workshop
ORGANIZER;CN="Research Training Group ALOP at Trier University":MAILTO:OptimizationDays@uni-trier.de
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